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In this paper, we propose a data-driven method to measure the impact of the 'woman card' exchange between Hillary Clinton and Donald Trump. Building from a unique dataset of the two candidates' Twitter followers, we first examine the transition dynamics of the two candidates' Twitter followers one week before the exchange and one week after. Then we train a convolutional neural network to classify the gender of the followers and unfollowers, and study how women in particular are reacting to the 'woman card' exchange. Our study suggests that the 'woman card' comment has made women more likely to follow Hillary Clinton, less likely to unfollow her and that it has apparently not affected the gender composition of Trump followers. | Pricing the Woman Card: Gender Politics between Hillary Clinton and
Donald Trump | 9,200 |
This exploratory study used the R Statistical Software to perform Monte Carlo simulation of time maps, which characterize events based on the elapsed time since the last event and the time that will transpire until the next event, and compare them to time maps from real Twitter users. Time maps are used to explore differences in the interarrival patterns of Tweets between human users, humans who use scheduling services like TweetDeck and HootSuite, and non-human (bot) users. The results indicate that there are differences between the tweet interarrival patterns across these categories of users, and that time maps could potentially be used to automate the detection of bot accounts on Twitter. This could enhance social media intelligence capabilities, help bot developers build more human-like Twitter bots to avoid detection, or both. | Bot or Not? Deciphering Time Maps for Tweet Interarrivals | 9,201 |
Dynamic networks are increasingly being usedd to model real world datasets. A challenging task in their analysis is to detect and characterize clusters. It is useful for analyzing real-world data such as detecting evolving communities in networks. We propose a temporal clustering framework based on a set of network generative models to address this problem. We use PARAFAC decomposition to learn network models from datasets.We then use $K$-means for clustering, the Silhouette criterion to determine the number of clusters, and a similarity score to order the clusters and retain the significant ones. In order to address the time-dependent aspect of these clusters, we propose a segmentation algorithm to detect their formations, dissolutions and lifetimes. Synthetic networks with ground truth and real-world datasets are used to test our method against state-of-the-art, and the results show that our method has better performance in clustering and lifetime detection than previous methods. | Temporal Clustering in Dynamic Networks with Tensor Decomposition | 9,202 |
This report analyses data collected from Last.fm and used to create a real-time recommendation system. We collected over 2M songs and 1M tags and 372K user's listening habits. We characterize users' profiles: age, playcount, friends, gender and country. We characterized song, artist and tag popularity, genres of songs. Additionally we evaluated the co-occurrence of songs in users' histories, which can be used to compute similarity between songs. | Mixtape Application: Last.fm Data Characterization | 9,203 |
This paper explores the social quality (goodness) of community structures formed across Twitter users, where social links within the structures are estimated based upon semantic properties of user-generated content (corpus). We examined the overlap of the community structures of the constructed graphs, and followership-based social communities, to find the social goodness of the links constructed. Unigram, bigram and LDA content models were empirically investigated for evaluation of effectiveness, as approximators of underlying social graphs, such that they maintain the {\it community} social property. Impact of content at varying granularities, for the purpose of predicting links while retaining the social community structures, was investigated. 100 discussion topics, spanning over 10 Twitter events, were used for experiments. The unigram language model performed the best, indicating strong similarity of word usage within deeply connected social communities. This observation agrees with the phenomenon of evolution of word usage behavior, that transform individuals belonging to the same community tending to choose the same words, made by Danescu et al. (2013), and raises a question on the literature that use, without validation, LDA for content-based social link prediction over other content models. Also, semantically finer-grained content was observed to be more effective compared to coarser-grained content. | Assessment of Effectiveness of Content Models for Approximating Twitter
Social Connection Structures | 9,204 |
In this paper, we study the likelihood of Bernie Sanders supporters voting for Donald Trump instead of Hillary Clinton. Building from a unique time-series dataset of the three candidates' Twitter followers, which we make public here, we first study the proportion of Sanders followers who simultaneously follow Trump (but not Clinton) and how this evolves over time. Then we train a convolutional neural network to classify the gender of Sanders followers, and study whether men are more likely to jump ship for Trump than women. Our study shows that between March and May an increasing proportion of Sanders followers are following Trump (but not Clinton). The proportion of Sanders followers who follow Clinton but not Trump has actually decreased. Equally important, our study suggests that the jumping ship behavior will be affected by gender and that men are more likely to switch to Trump than women. | Will Sanders Supporters Jump Ship for Trump? Fine-grained Analysis of
Twitter Followers | 9,205 |
Algorithms for mining very large graphs, such as those representing online social networks, to discover the relative frequency of small subgraphs within them are of high interest to sociologists, computer scientists and marketeers alike. However, the computation of these network motif statistics via naive enumeration is infeasible for either its prohibitive computational costs or access restrictions on the full graph data. Methods to estimate the motif statistics based on random walks by sampling only a small fraction of the subgraphs in the large graph address both of these challenges. In this paper, we present a new algorithm, called the Waddling Random Walk (WRW), which estimates the concentration of motifs of any size. It derives its name from the fact that it sways a little to the left and to the right, thus also sampling nodes not directly on the path of the random walk. The WRW algorithm achieves its computational efficiency by not trying to enumerate subgraphs around the random walk but instead using a randomized protocol to sample subgraphs in the neighborhood of the nodes visited by the walk. In addition, WRW achieves significantly higher accuracy (measured by the closeness of its estimate to the correct value) and higher precision (measured by the low variance in its estimations) than the current state-of-the-art algorithms for mining subgraph statistics. We illustrate these advantages in speed, accuracy and precision using simulations on well-known and widely used graph datasets representing real networks. | Waddling Random Walk: Fast and Accurate Mining of Motif Statistics in
Large Graphs | 9,206 |
Coupling cyber and physical systems gives rise to numerous engineering challenges and opportunities. An important challenge is the contagion of failure from one system to another, that can lead to large scale cascading failures. On the other hand, self-healing ability emerges as a valuable opportunity where the overlay cyber network can cure failures in the underlying physical network. To capture both self-healing and contagion, we introduce a factor graph representation of inter-dependent cyber-physical systems in which factor nodes represent various node functionalities and the edges capture the interactions between the nodes. We develop a message passing algorithm to study the dynamics of failure propagation and healing in this representation. Through applying a fixed-point analysis to this algorithm, we investigate the network reaction to initial disruptions. Our analysis provides simple yet critical guidelines for choosing network parameters to achieve resiliency against cascading failures. | Message Passing for Analysis and Resilient Design of Self-Healing
Interdependent Cyber-Physical Networks | 9,207 |
With the recent emergence and the explosion of users in social networks, Internet users have come to know more about other people or companies through information obtained from online groups or communities and therefore meet new opportunities. An avalanche of diverse and unstructured information is created every minute, for billions of devices connected to the Internet all the time. Analyze, process, understand and evaluate these data, it is a challenging task, given the volume, velocity and variety of information from these social networks. However, a better understanding of the process of monitoring networks and the use of tools of this kind are essential for people, companies and the academic world. The application of monitoring systems, information management and the possibility of bringing brands to people, are some positives that this research will provide better knowledge of the problems. In this research will be analyzed monitoring tool of social networks SCUP, its details, its features and the tool related to business management. In conclusion, we present all the peculiarities of the software and the possibilities applicable in the corporate environment, as aid to decision making. | Monitoramento de redes sociais analise de ferramenta com foco no retorno
estrategico empresarial (Monitoring social networks analysis tool focused on
strategic business return) | 9,208 |
Accumulation of large amount of location-specific reviews on web due to escalating popularity of Location-based Social Networking platforms like Yelp, Foursquare, Brightkite etc. in recent years, has created the opportunity to discover location-specific activities and develop myriads of location-aware activity recommendation applications. The performance and popularity of such recommendation applications greatly depend on the richness and accuracy of the back-end knowledgebase, which intern is regulated by information relevancy and redundancy issues. Existing work on activity discovery have not made any attempt to ensure relevancy and non-redundancy of discovered knowledge (i.e., location-specific activities). Moreover, majority of these work have utilized body-worn sensors, images or human GPS traces and discovered generalized activities that do not convey any location-specific knowledge. In this thesis, we address the mentioned issues with serious concern and propose an effective solution to discover Location-specific Activity Network, in short LANet from location-aware reviews. The information network LANet serves as an accurate, enriched and unified knowledgebase of a Location-aware Activity Recommendation System. While building LANet, we also introduce novel ideas like, activity-based location similarity detection and measuring uniqueness, generality/speciality of an activity at a particular location to enrich the said knowledge base to a great extent. Experimental results show the information richness and accuracy of the proposed knowledge base which is comparable to human perception and accounts for our success in achieving the desired solution. | LANet: An Enriched Knowledgebase for Location-aware Activity
Recommendation System | 9,209 |
Campaigners, advertisers and activists are increasingly turning to social recommendation mechanisms, provided by social media, for promoting their products, services, brands and even ideas. However, many times, such social network based campaigns perform poorly in practice because the intensity of the recommendations drastically reduces beyond a few hops from the source. A natural strategy for maintaining the intensity is to provide incentives. In this paper, we address the problem of minimizing the cost incurred by the campaigner for incentivizing a fraction of individuals in the social network, while ensuring that the campaign message reaches a given expected fraction of individuals. We also address the dual problem of maximizing the campaign penetration for a resource constrained campaigner. To help us understand and solve the above mentioned problems, we use percolation theory to formally state them as optimization problems. These problems are not amenable to traditional approaches because of a fixed point equation that needs to be solved numerically. However, we use results from reliability theory to establish some key properties of the fixed point, which in turn enables us to solve these problems using algorithms that are linearithmic in maximum node degree. Furthermore, we evaluate the efficacy of the analytical solution by performing simulations on real world networks. | Incentivized Campaigning in Social Networks | 9,210 |
The automatic content analysis of mass media in the social sciences has become necessary and possible with the raise of social media and computational power. One particularly promising avenue of research concerns the use of sentiment analysis in microblog streams. However, one of the main challenges consists in aggregating sentiment polarity in a timely fashion that can be fed to the prediction method. We investigated a large set of sentiment aggregate functions and performed a regression analysis using political opinion polls as gold standard. Our dataset contains nearly 233 000 tweets, classified according to their polarity (positive, negative or neutral), regarding the five main Portuguese political leaders during the Portuguese bailout (2011-2014). Results show that different sentiment aggregate functions exhibit different feature importance over time while the error keeps almost unchanged. | Sentiment Aggregate Functions for Political Opinion Polling using
Microblog Streams | 9,211 |
In recent years, what has become known as collaborative consumption has undergone rapid expansion through peer-to-peer (P2P) platforms. In the field of tourism, a particularly notable example is that of Airbnb. This article analyses the spatial patterns of Airbnb in Barcelona and compares them with hotels and sightseeing spots. New sources of data, such as Airbnb listings and geolocated photographs are used. Analysis of bivariate spatial autocorrelation reveals a close spatial relationship between Airbnb and hotels, with a marked centre-periphery pattern, although Airbnb predominates around the main hotel axis and hotels predominate in some peripheral areas of the city. Another interesting finding is that Airbnb capitalises more on the advantages of proximity to the main tourist attractions of the city than does the hotel sector. Finally, it was possible to detect those parts of the city that have seen the greatest increase in pressure from tourism related to Airbnb's recent expansion. | Airbnb in tourist cities: comparing spatial patterns of hotels and
peer-to-peer accommodation | 9,212 |
The accelerated growth of mobile trajectories in location-based services brings valuable data resources to understand users' moving behaviors. Apart from recording the trajectory data, another major characteristic of these location-based services is that they also allow the users to connect whomever they like. A combination of social networking and location-based services is called as location-based social networks (LBSN). As shown in previous works, locations that are frequently visited by socially-related persons tend to be correlated, which indicates the close association between social connections and trajectory behaviors of users in LBSNs. In order to better analyze and mine LBSN data, we present a novel neural network model which can joint model both social networks and mobile trajectories. In specific, our model consists of two components: the construction of social networks and the generation of mobile trajectories. We first adopt a network embedding method for the construction of social networks: a networking representation can be derived for a user. The key of our model lies in the component of generating mobile trajectories. We have considered four factors that influence the generation process of mobile trajectories, namely user visit preference, influence of friends, short-term sequential contexts and long-term sequential contexts. To characterize the last two contexts, we employ the RNN and GRU models to capture the sequential relatedness in mobile trajectories at different levels, i.e., short term or long term. Finally, the two components are tied by sharing the user network representations. Experimental results on two important applications demonstrate the effectiveness of our model. Especially, the improvement over baselines is more significant when either network structure or trajectory data is sparse. | A Neural Network Approach to Joint Modeling Social Networks and Mobile
Trajectories | 9,213 |
With the increasing use of online communication platforms, such as email, twitter, and messaging applications, we are faced with a growing amount of data that combine content (what is said), time (when), and user (by whom) information. An important computational challenge is to analyze these data, discover meaningful patterns, and understand what is happening. We consider the problem of mining online communication data and finding top-k temporal events. We define a temporal event to be a coherent topic that is discussed frequently, in a relatively short time span, while the information ow of the event respects the underlying network structure. We construct our model for detecting temporal events in two steps. We first introduce the notion of interaction meta-graph, which connects associated interactions. Using this notion, we define a temporal event to be a subset of interactions that (i) are topically and temporally close and (ii) correspond to a tree that captures the information ow. Finding the best temporal event leads to budget version of the prize-collecting Steiner-tree (PCST) problem, which we solve using three different methods: a greedy approach, a dynamic-programming algorithm, and an adaptation to an existing approximation algorithm. The problem of finding the top- k events among a set of candidate events maps to maximum set-cover problem, and thus, solved by greedy. We compare and analyze our algorithms in both synthetic and real datasets, such as twitter and email communication. The results show that our methods are able to detect meaningful temporal events. | Discovering topically- and temporally-coherent events in interaction
networks | 9,214 |
Motivated by the need to extract meaning from large amounts of complex structured data, we consider three critical problems on graphs: localization, decomposition, and dictionary learning of piecewise-constant signals. These graph-based problems are related to many real-world applications, such as localizing stimulus in brain connectivity networks, and mining traffic events in city street networks, where the key issue is to find the supports of localized activated patterns. Counterparts of these problems in classical signal/image processing, such as impulse detection and foreground detection, have been studied over the past few decades. We use piecewise-constant graph signals to model localized patterns, where each piece indicates a localized pattern that exhibits homogeneous internal behavior and the number of pieces indicates the number of localized patterns. For such signals, we show that decomposition and dictionary learning are natural extensions of localization, the goal of which is not only to efficiently approximate graph signals, but also to accurately find supports of localized patterns. For each of the three problems, i.e., localization, decomposition, and dictionary learning, we propose a specific graph signal model, an optimization problem, and a computationally efficient solver. The proposed solvers directly find the supports of arbitrary localized activated patterns without tuning any thresholds. We then conduct an extensive empirical study to validate the proposed methods on both simulated and real data including the analysis of a large volume of spatio-temporal Manhattan urban data. The analysis validates the effectiveness of the approach and suggests that graph signal processing tools may aid in urban planning and traffic forecasting. | Localization, Decomposition, and Dictionary Learning of
Piecewise-Constant Signals on Graphs | 9,215 |
Mobile dating apps have become a popular means to meet potential partners. Although several exist, one recent addition stands out amongst all others. Tinder presents its users with pictures of people geographically nearby, whom they can either like or dislike based on first impressions. If two users like each other, they are allowed to initiate a conversation via the chat feature. In this paper we use a set of curated profiles to explore the behaviour of men and women in Tinder. We reveal differences between the way men and women interact with the app, highlighting the strategies employed. Women attain large numbers of matches rapidly, whilst men only slowly accumulate matches. To expand on our findings, we collect survey data to understand user intentions on Tinder. Most notably, our results indicate that a little effort in grooming profiles, especially for male users, goes a long way in attracting attention. | A First Look at User Activity on Tinder | 9,216 |
Complex networks usually expose community structure with groups of nodes sharing many links with the other nodes in the same group and relatively few with the nodes of the rest. This feature captures valuable information about the organization and even the evolution of the network. Over the last decade, a great number of algorithms for community detection have been proposed to deal with the increasingly complex networks. However, the problem of doing this in a private manner is rarely considered. In this paper, we solve this problem under differential privacy, a prominent privacy concept for releasing private data. We analyze the major challenges behind the problem and propose several schemes to tackle them from two perspectives: input perturbation and algorithm perturbation. We choose Louvain method as the back-end community detection for input perturbation schemes and propose the method LouvainDP which runs Louvain algorithm on a noisy super-graph. For algorithm perturbation, we design ModDivisive using exponential mechanism with the modularity as the score. We have thoroughly evaluated our techniques on real graphs of different sizes and verified their outperformance over the state-of-the-art. | Detecting Communities under Differential Privacy | 9,217 |
In this work, we tackle the problem of predicting entity popularity on Twitter based on the news cycle. We apply a supervised learn- ing approach and extract four types of features: (i) signal, (ii) textual, (iii) sentiment and (iv) semantic, which we use to predict whether the popularity of a given entity will be high or low in the following hours. We run several experiments on six different entities in a dataset of over 150M tweets and 5M news and obtained F1 scores over 0.70. Error analysis indicates that news perform better on predicting entity popularity on Twitter when they are the primary information source of the event, in opposition to events such as live TV broadcasts, political debates or football matches. | Learning from the News: Predicting Entity Popularity on Twitter | 9,218 |
Inspired by the work of [Kempe, Kleinberg, Oren, Slivkins, EC13] we introduce and analyze a model on opinion formation; the update rule of our dynamics is a simplified version of that of Kempe et. al. We assume that the population is partitioned into types whose interaction pattern is specified by a graph. Interaction leads to population mass moving from types of smaller mass to those of bigger. We show that starting uniformly at random over all population vectors on the simplex, our dynamics converges point-wise with probability one to an independent set. This settles an open problem of Kempe et. al., as applicable to our dynamics. We believe that our techniques can be used to settle the open problem for the Kempe et. al. dynamics as well. Next, we extend the model of Kempe et. al. by introducing the notion of birth and death of types, with the interaction graph evolving appropriately. Birth of types is determined by a Bernoulli process and types die when their population mass is less than a parameter $\epsilon$. We show that if the births are infrequent, then there are long periods of "stability" in which there is no population mass that moves. Finally we show that even if births are frequent and "stability" is not attained, the total number of types does not explode: it remains logarithmic in $1/\epsilon$. | Opinion Dynamics in Networks: Convergence, Stability and Lack of
Explosion | 9,219 |
The serious privacy and security problems related to online social networks (OSNs) are what fueled two complementary studies as part of this thesis. In the first study, we developed a general algorithm for the mining of data of targeted organizations by using Facebook (currently the most popular OSN) and socialbots. By friending employees in a targeted organization, our active socialbots were able to find new employees and informal organizational links that we could not find by crawling with passive socialbots. We evaluated our method on the Facebook OSN and were able to reconstruct the social networks of employees in three distinct, actual organizations. Furthermore, in the crawling process with our active socialbots we discovered up to 13.55% more employees and 22.27% more informal organizational links in contrast to the crawling process that was performed by passive socialbots with no company associations as friends. In our second study, we developed a general algorithm for reaching specific OSN users who declared themselves to be employees of targeted organizations, using the topologies of organizational social networks and utilizing socialbots. We evaluated the proposed method on targeted users from three actual organizations on Facebook, and two actual organizations on the Xing OSN (another popular OSN platform). Eventually, our socialbots were able to reach specific users with a success rate of up to 70% on Facebook, and up to 60% on Xing. | The Security of Organizations and Individuals in Online Social Networks | 9,220 |
Big Data dealing with the social produce predictive correlations for the benefit of brands and web platforms. Beyond "society" and "opinion" for which the text lays out a genealogy, appear the "traces" that must be theorized as "replications" by the social sciences in order to reap the benefits of the uncertain status of entities' widespread traceability. High frequency replications as a collective phenomenon did exist before the digital networks emergence but now they leave traces that can be computed. The third generation of Social Sciences currently emerging must assume the specific nature of the world of data created by digital networks, without reducing them to the categories of the sciences of "society" or "opinion". Examples from recent works on Twitter and other digital corpora show how the search for structural effects or market-style trade-offs are prevalent even though insights about propagation, virality and memetics could help build a new theoretical framework. | Big data challenges for the social sciences: from society and opinion to
replications | 9,221 |
Crowdfunding platforms have become important sites where people can create projects to seek funds toward turning their ideas into products, and back someone else's projects. As news media have reported successfully funded projects (e.g., Pebble Time, Coolest Cooler), more people have joined crowdfunding platforms and launched projects. But in spite of rapid growth of the number of users and projects, a project success rate at large has been decreasing because of launching projects without enough preparation and experience. Little is known about what reactions project creators made (e.g., giving up or making the failed projects better) when projects failed, and what types of successful projects we can find. To solve these problems, in this manuscript we (i) collect the largest datasets from Kickstarter, consisting of all project profiles, corresponding user profiles, projects' temporal data and users' social media information; (ii) analyze characteristics of successful projects, behaviors of users and understand dynamics of the crowdfunding platform; (iii) propose novel statistical approaches to predict whether a project will be successful and a range of expected pledged money of the project; (iv) develop predictive models and evaluate performance of the models; (v) analyze what reactions project creators had when project failed, and if they did not give up, how they made the failed projects successful; and (vi) cluster successful projects by their evolutional patterns of pledged money toward understanding what efforts project creators should make in order to get more pledged money. Our experimental results show that the predictive models can effectively predict project success and a range of expected pledged money. | How to Succeed in Crowdfunding: a Long-Term Study in Kickstarter | 9,222 |
We perform a large-scale analysis of third-party trackers on the World Wide Web from more than 3.5 billion web pages of the CommonCrawl 2012 corpus. We extract a dataset containing more than 140 million third-party embeddings in over 41 million domains. To the best of our knowledge, this constitutes the largest web tracking dataset collected so far, and exceeds related studies by more than an order of magnitude in the number of domains and web pages analyzed. We perform a large-scale study of online tracking, on three levels: (1) On a global level, we give a precise figure for the extent of tracking, give insights into the structure of the `online tracking sphere' and analyse which trackers are used by how many websites. (2) On a country-specific level, we analyse which trackers are used by websites in different countries, and identify the countries in which websites choose significantly different trackers than in the rest of the world. (3) We answer the question whether the content of websites influences the choice of trackers they use, leveraging more than 90 thousand categorized domains. In particular, we analyse whether highly privacy-critical websites make different choices of trackers than other websites. Based on the performed analyses, we confirm that trackers are widespread (as expected), and that a small number of trackers dominates the web (Google, Facebook and Twitter). In particular, the three tracking domains with the highest PageRank are all owned by Google. The only exception to this pattern are a few countries such as China and Russia. Our results suggest that this dominance is strongly associated with country-specific political factors such as freedom of the press. We also confirm that websites with highly privacy-critical content are less likely to contain trackers (60% vs 90% for other websites), even though the majority of them still do contain trackers. | On the Ubiquity of Web Tracking: Insights from a Billion-Page Web Crawl | 9,223 |
The publication of fake reviews by parties with vested interests has become a severe problem for consumers who use online product reviews in their decision making. To counter this problem a number of methods for detecting these fake reviews, termed opinion spam, have been proposed. However, to date, many of these methods focus on analysis of review text, making them unsuitable for many review systems where accom-panying text is optional, or not possible. Moreover, these approaches are often computationally expensive, requiring extensive resources to handle text analysis over the scale of data typically involved. In this paper, we consider opinion spammers manipulation of average ratings for products, focusing on dif-ferences between spammer ratings and the majority opinion of honest reviewers. We propose a lightweight, effective method for detecting opinion spammers based on these differences. This method uses binomial regression to identify reviewers having an anomalous proportion of ratings that deviate from the majority opinion. Experiments on real-world and synthetic data show that our approach is able to successfully iden-tify opinion spammers. Comparison with the current state-of-the-art approach, also based only on ratings, shows that our method is able to achieve similar detection accuracy while removing the need for assump-tions regarding probabilities of spam and non-spam reviews and reducing the heavy computation required for learning. | Detection of opinion spam based on anomalous rating deviation | 9,224 |
The challenge of associating entities across multiple domains is a key problem in social media understanding. Successful cross-domain entity resolution provides integration of information from multiple sites to create a complete picture of user and community activities, characteristics, and trends. In this work, we examine the problem of entity resolution across Twitter and Instagram using general techniques. Our methods fall into three categories: profile, content, and graph based. For the profile-based methods, we consider techniques based on approximate string matching. For content-based methods, we perform author identification. Finally, for graph-based methods, we apply novel cross-domain community detection methods and generate neighborhood-based features. The three categories of methods are applied to a large graph of users in Twitter and Instagram to understand challenges, determine performance, and understand fusion of multiple methods. Final results demonstrate an equal error rate less than 1%. | Cross-Domain Entity Resolution in Social Media | 9,225 |
An essential feature of large scale free graphs, such as the Web, protein-to-protein interaction, brain connectivity, and social media graphs, is that they tend to form recursive communities. The latter are densely connected vertex clusters exhibiting quick local information dissemination and processing. Under the fuzzy graph model vertices are fixed while each edge exists with a given probability according to a membership function. This paper presents Fuzzy Walktrap and Fuzzy Newman-Girvan, fuzzy versions of two established community discovery algorithms. The proposed algorithms have been applied to a synthetic graph generated by the Kronecker model with different termination criteria and the results are discussed. | On converting community detection algorithms for fuzzy graphs in Neo4j | 9,226 |
Modeling interpersonal influence on different sentimental polarities is a fundamental problem in opinion formation and viral marketing. There has not been seen an effective solution for learning sentimental influences from users' behaviors yet. Previous related works on information propagation directly define interpersonal influence between each pair of users as a parameter, which is independent from each others, even if the influences come from or affect the same user. And influences are learned from user's propagation behaviors, namely temporal cascades, while sentiments are not associated with them. Thus we propose to model the interpersonal influence by latent influence and susceptibility matrices defined on individual users and sentiment polarities. Such low-dimensional and distributed representations naturally make the interpersonal influences related to the same user coupled with each other, and in turn, reduce the model complexity. Sentiments act on different rows of parameter matrices, depicting their effects in modeling cascades. With the iterative optimization algorithm of projected stochastic gradient descent over shuffled mini-batches and Adadelta update rule, negative cases are repeatedly sampled with the distribution of infection frequencies users, for reducing computation cost and optimization imbalance. Experiments are conducted on Microblog dataset. The results show that our model achieves better performance than the state-of-the-art and pair-wise models. Besides, analyzing the distribution of learned users' sentimental influences and susceptibilities results some interesting discoveries. | Learning Sentimental Influences from Users' Behaviors | 9,227 |
With the recent advance of micro-blogs and social networks, people can view and post comments on the websites in a very convenient way. However, it is also a big concern that the malicious users keep polluting the cyber environment by scamming, spamming or repeatedly advertising. So far the most common way to detect and report malicious comments is based on voluntary reviewing from honest users. To encourage contribution, very often some non-monetary credits will be given to an honest user who validly reports a malicious comment. In this note we argue that such credit-based incentive mechanisms should fail in most cases: if reporting a malicious comment receives diminishing revenue, then in the long term no rational honest user will participate in comment reviewing. | On Credit-based Incentive Mechanisms of Voluntary User Comment Reviewing
in Social Networks | 9,228 |
Network reliability is a well-studied problem that requires to measure the probability that a target node is reachable from a source node in a probabilistic (or uncertain) graph, i.e., a graph where every edge is assigned a probability of existence. Many approaches and problem variants have been considered in the literature, all assuming that edge-existence probabilities are fixed. Nevertheless, in real-world graphs, edge probabilities typically depend on external conditions. In metabolic networks a protein can be converted into another protein with some probability depending on the presence of certain enzymes. In social influence networks the probability that a tweet of some user will be re-tweeted by her followers depends on whether the tweet contains specific hashtags. In transportation networks the probability that a network segment will work properly or not might depend on external conditions such as weather or time of the day. In this paper we overcome this limitation and focus on conditional reliability, that is assessing reliability when edge-existence probabilities depend on a set of conditions. In particular, we study the problem of determining the k conditions that maximize the reliability between two nodes. We deeply characterize our problem and show that, even employing polynomial-time reliability-estimation methods, it is NP-hard, does not admit any PTAS, and the underlying objective function is non-submodular. We then devise a practical method that targets both accuracy and efficiency. We also study natural generalizations of the problem with multiple source and target nodes. An extensive empirical evaluation on several large, real-life graphs demonstrates effectiveness and scalability of the proposed methods. | Conditional Reliability in Uncertain Graphs | 9,229 |
When an epidemic spreads in a network, a key question is where was its source, i.e., the node that started the epidemic. If we know the time at which various nodes were infected, we can attempt to use this information in order to identify the source. However, maintaining observer nodes that can provide their infection time may be costly, and we may have a budget $k$ on the number of observer nodes we can maintain. Moreover, some nodes are more informative than others due to their location in the network. Hence, a pertinent question arises: Which nodes should we select as observers in order to maximize the probability that we can accurately identify the source? Inspired by the simple setting in which the node-to-node delays in the transmission of the epidemic are deterministic, we develop a principled approach for addressing the problem even when transmission delays are random. We show that the optimal observer-placement differs depending on the variance of the transmission delays and propose approaches in both low- and high-variance settings. We validate our methods by comparing them against state-of-the-art observer-placements and show that, in both settings, our approach identifies the source with higher accuracy. | Observer Placement for Source Localization: The Effect of Budgets and
Transmission Variance | 9,230 |
YouTube draws large number of users who contribute actively by uploading videos or commenting on existing videos. However, being a crowd sourced and large content pushed onto it, there is limited control over the content. This makes malicious users push content (videos and comments) which is inappropriate (unsafe), particularly when such content is placed around cartoon videos which are typically watched by kids. In this paper, we focus on presence of unsafe content for children and users who promote it. For detection of child unsafe content and its promoters, we perform two approaches, one based on supervised classification which uses an extensive set of video-level, user-level and comment-level features and another based Convolutional Neural Network using video frames. Detection accuracy of 85.7% is achieved which can be leveraged to build a system to provide a safe YouTube experience for kids. Through detailed characterization studies, we are able to successfully conclude that unsafe content promoters are less popular and engage less as compared with other users. Finally, using a network of unsafe content promoters and other users based on their engagements (likes, subscription and playlist addition) and other factors, we find that unsafe content is present very close to safe content and unsafe content promoters form very close knit communities with other users, thereby further increasing the likelihood of a child getting getting exposed to unsafe content. | KidsTube: Detection, Characterization and Analysis of Child Unsafe
Content & Promoters on YouTube | 9,231 |
Given an aftermath of a cascade in the network, i.e. a set $V_I$ of "infected" nodes after an epidemic outbreak or a propagation of rumors/worms/viruses, how can we infer the sources of the cascade? Answering this challenging question is critical for computer forensic, vulnerability analysis, and risk management. Despite recent interest towards this problem, most of existing works focus only on single source detection or simple network topologies, e.g. trees or grids. In this paper, we propose a new approach to identify infection sources by searching for a seed set $S$ that minimizes the \emph{symmetric difference} between the cascade from $S$ and $V_I$, the given set of infected nodes. Our major result is an approximation algorithm, called SISI, to identify infection sources \emph{without the prior knowledge on the number of source nodes}. SISI, to our best knowledge, is the first algorithm with \emph{provable guarantee} for the problem in general graphs. It returns a $\frac{2}{(1-\epsilon)^2}\Delta$-approximate solution with high probability, where $\Delta$ denotes the maximum number of nodes in $V_I$ that may infect a single node in the network. Our experiments on real-world networks show the superiority of our approach and SISI in detecting true source(s), boosting the F1-measure from few percents, for the state-of-the-art NETSLEUTH, to approximately 50\%. | Multiple Infection Sources Identification with Provable Guarantees | 9,232 |
Ensuring the early detection of important social network users is a challenging task. Some peer ranking services are now well established, such as PeerIndex, Klout, or Kred. Their function is to rank users according to their influence. This notion of influence is however abstract, and the algorithms achieving this ranking are opaque. Following the rising demand for a more transparent web, we explore the problem of gaining knowledge by reverse engineering such peer ranking services, with regards to the social network topology they get as an input. Since these services exploit the online activity of users (and therefore their connectivity in social networks), we provide a precise evaluation of how topological metrics of the social network impact the final user ranking. Our approach is the following : we first model the ranking service as a black-box with which we interact by creating user profiles and by performing operations on them. Through those profiles, we trigger some slight topological modifications. By monitoring the impact of these modifications on the rankings of those profiles, we infer the weight of each topological metric in the black-box, thus reversing the service influence cookbook. | Uncovering Influence Cookbooks : Reverse Engineering the Topological
Impact in Peer Ranking Services | 9,233 |
User's home locations are used by numerous social media applications, such as social media analysis. However, since the user's home location is not generally open to the public, many researchers have been attempting to develop a more accurate home location estimation. A social network that expresses relationships between users is used to estimate the users' home locations. The network-based home location estimation method with iteration, which propagates the estimated locations, is used to estimate more users' home locations. In this study, we analyze the function of network-based home location estimation with iteration while using the social network based on following relationships on Twitter. The results indicate that the function that selects the most frequent location among the friends' location has the best accuracy. Our analysis also shows that the 88% of users, who are in the social network based on following relationships, has at least one correct home location within one-hop (friends and friends of friends). According to this characteristic of the social network, we indicate that twice is sufficient for iteration. | Analysis of Home Location Estimation with Iteration on Twitter Following
Relationship | 9,234 |
Although influence maximization problem has been extensively studied over the past ten years, majority of existing work adopt one of the following models: \emph{full-feedback model} or \emph{zero-feedback model}. In the zero-feedback model, we have to commit the seed users all at once in advance, this strategy is also known as non-adaptive policy. In the full-feedback model, we select one seed at a time and wait until the diffusion completes, before selecting the next seed. Full-feedback model has better performance but potentially huge delay, zero-feedback model has zero delay but poorer performance since it does not utilize the observation that may be made during the seeding process. To fill the gap between these two models, we propose \emph{Partial-feedback Model}, which allows us to select a seed at any intermediate stage. We develop two novel greedy policies that, for the first time, achieve bounded approximation ratios under both uniform and non-uniform cost settings. | No Time to Observe: Adaptive Influence Maximization with Partial
Feedback | 9,235 |
Gun related violence is a complex issue and accounts for a large proportion of violent incidents. In the research reported in this paper, we set out to investigate the pro-gun and anti-gun sentiments expressed on a social media platform, namely Twitter, in response to the 2012 Sandy Hook Elementary School shooting in Connecticut, USA. Machine learning techniques are applied to classify a data corpus of over 700,000 tweets. The sentiments are captured using a public sentiment score that considers the volume of tweets as well as population. A web-based interactive tool is developed to visualise the sentiments and is available at http://www.gunsontwitter.com. The key findings from this research are: (i) There are elevated rates of both pro-gun and anti-gun sentiments on the day of the shooting. Surprisingly, the pro-gun sentiment remains high for a number of days following the event but the anti-gun sentiment quickly falls to pre-event levels. (ii) There is a different public response from each state, with the highest pro-gun sentiment not coming from those with highest gun ownership levels but rather from California, Texas and New York. | A Machine Learning Analysis of Twitter Sentiment to the Sandy Hook
Shootings | 9,236 |
This research presents an analytical model that aims to pin-point influential posts across a social web comprised of a corpus of posts. The model employs the Latent Dirichlet Al-location algorithm to associate posts with topics, and the TF-IDF metric to identify the key posts associated with each top-ic. The model was demonstrated in the domain of customer relationship by enabling careful monitoring of evolving "storms" created by individuals which tend to impact large audiences (either positively or negatively). Future research should be engaged in order to extend the scope of the corpus by including additional relevant publicly available sources. | Pinpoint Influential Posts and Authors | 9,237 |
Networks extracted from social media platforms frequently include multiple types of links that dynamically change over time; these links can be used to represent dyadic interactions such as economic transactions, communications, and shared activities. Organizing this data into a dynamic multiplex network, where each layer is composed of a single edge type linking the same underlying vertices, can reveal interesting cross-layer interaction patterns. In coevolving networks, links in one layer result in an increased probability of other types of links forming between the same node pair. Hence we believe that a holistic approach in which all the layers are simultaneously considered can outperform a factored approach in which link prediction is performed separately in each layer. This paper introduces a comprehensive framework, MLP (Multilayer Link Prediction), in which link existence likelihoods for the target layer are learned from the other network layers. These likelihoods are used to reweight the output of a single layer link prediction method that uses rank aggregation to combine a set of topological metrics. Our experiments show that our reweighting procedure outperforms other methods for fusing information across network layers. | A Holistic Approach for Predicting Links in Coevolving Multilayer
Networks | 9,238 |
Growing interest in modeling large, complex networks has spurred significant research into generative graph models. Kronecker-style models (SKG and R-MAT) are often used due to their scalability and ability to mimic key properties of real-world networks. Although a few papers theoretically establish these models' behavior for specific parameters, many claims used to justify their use are supported only empirically. In this work, we prove several results using asymptotic analysis which illustrate that empirical studies may not fully capture the true behavior of the models. Paramount to the widespread adoption of Kronecker-style models was the introduction of a linear-time edge-sampling variant (R-MAT), which existing literature typically treats as interchangeable with SKG. We prove that although several R-MAT formulations are asymptotically equivalent, their behavior diverges from that of SKG. Further, we show these results are observable even at relatively small graph sizes. Second, we consider a case where asymptotic analysis reveals unexpected behavior within a given model. | Asymptotic Analysis of Equivalences and Core-Structures in
Kronecker-Style Graph Models | 9,239 |
In this paper we aim at understanding if and how, by analysing people's profile and historical data (such as data available on Facebook profiles and interactions, or collected explicitly) we can motivate two persons to interact and eventually create long-term bonds. We do this by exploring the relationship between connectedness, social interactions and common life points on Facebook. The results are of particular importance for the development of technology that aims at reducing social isolation for people with less chances to interact, such as older adults. | What makes people bond?: A study on social interactions and common life
points on Facebook | 9,240 |
Patterns of event propagation in online social networks provide novel insights on the modeling and analysis of information dissemination over networks and physical systems. This paper studies the importance of follower links for event propagation on Twitter. Three recent event propagation traces are collected with the Twitter user language field being used to identify the Network of Networks (NoN) structure embedded in the Twitter follower networks. We first formulate event propagation on Twitter as an iterative state equation, and then propose an effective score function on follower links accounting for the containment of event propagation via link removals. Furthermore, we find that utilizing the NoN model can successfully identify influential follower links such that their removals lead to a remarkable reduction in event propagation on Twitter follower networks. Experimental results find that the between-network follower links, though only account for a small portion of the total follower links, are crucial to event propagation on Twitter. | Identifying Influential Links for Event Propagation on Twitter: A
Network of Networks Approach | 9,241 |
Studying information diffusion and the spread of goods in the real world and in many digital services can be extremely difficult since information about the information flows is challenging to accurately track. How information spreads has commonly been analysed from the perspective of homophily, social influence, and initial seed selection. However, in virtual worlds and virtual economies, the movements of information and goods can be precisely tracked. Therefore, these environments create laboratories for the accurate study of information diffusion characteristics that have been difficult to study in prior research. In this paper, we study how content visibility as well as sender and receiver characteristics, the relationship between them, and the types of multilayer social network layers affect content absorption and diffusion in virtual world. The results show that prior visibility of distributed content is the strongest predictor of content adoption and its further spread across networks. Among other analysed factors, the mechanics of diffusion, content quality, and content adoption by users neighbours on the social activity layer had very strong influences on the adoption of new content. | A picture is worth a thousand words: an empirical study on the influence
of content visibility on diffusion processes within a virtual world | 9,242 |
Crowdsourcing platforms have become quite popular due to the increasing demand of human computation-based tasks. Though the crowdsourcing systems are primarily demand-driven like MTurk, supply-driven marketplaces are becoming increasingly popular. Fiverr is a fast growing supply-driven marketplace where the sellers post micro-tasks (gigs) and users purchase them for prices as low as $5. In this paper, we study the Fiverr platform as a unique marketplace and characterize the sellers, buyers and the interactions among them. We find that sellers are more appeasing in their interactions and try to woo their buyers into buying their gigs. There are many small tightly-knit communities existing in the seller-seller network who support each other. We also study Fiverr as a seller-driven marketplace in terms of sales, churn rates, competitiveness among various sub-categories etc. and observe that while there are certain similarities with common marketplaces there are also many differences. | A Large-scale Analysis of the Marketplace Characteristics in Fiverr | 9,243 |
In this paper, we provide a quantitative and qualitative analyses of the viral tweets related to the US presidential election. In our study, we focus on analyzing the most retweeted 50 tweets for everyday during September and October 2016. The resulting set is composed 3,050 viral tweets, and they were retweeted over 20.5 million times. We manually annotated the tweets as favorable of Trump, Clinton, or neither. Our quantitative study shows that tweets favoring Trump were usually retweeted more than pro-Clinton tweets, with the exception of a few days in September and two days in October, especially the day following the first presidential debate and following the release of the Access Hollywood tape. On two days in October 2016, pro-Trump tweet volume accounted for than 90\% of the total tweet volume. | Trump vs. Hillary Analyzing Viral Tweets during US Presidential
Elections 2016 | 9,244 |
Despite an extensive literature has been devoted to mine and model mobility features, forecasting where, when and whom people will encounter/colocate still deserve further research efforts. Forecasting people's encounter and colocation features is the key point for the success of many applications ranging from epidemiology to the design of new networking paradigms and services such as delay tolerant and opportunistic networks. While many algorithms which rely on both mobility and social information have been proposed, we propose a novel encounter and colocation predictive model which predicts user's encounter and colocation events and their features by exploiting the spatio-temporal regularity in the history of these events. We adopt weighted features Bayesian predictor and evaluate its accuracy on two large scales WiFi and cellular datasets. Results show that our approach could improve prediction accuracy w.r.t standard naive Bayesian and some of the state-of-the-art predictors. | Predicting encounter and colocation events in metropolitan areas | 9,245 |
Discovering dense subgraphs and understanding the relations among them is a fundamental problem in graph mining. We want to not only identify dense subgraphs, but also build a hierarchy among them (e.g., larger but sparser subgraphs formed by two smaller dense subgraphs). Peeling algorithms (k-core, k-truss, and nucleus decomposition) have been effective to locate many dense subgraphs. However, constructing a hierarchical representation of density structure, even correctly computing the connected k-cores and k-trusses, have been mostly overlooked. Keeping track of connected components during peeling requires an additional traversal operation, which is as expensive as the peeling process. In this paper, we start with a thorough survey and point to nuances in problem formulations that lead to significant differences in runtimes. We then propose efficient and generic algorithms to construct the hierarchy of dense subgraphs for k-core, k-truss, or any nucleus decomposition. Our algorithms leverage the disjoint-set forest data structure to efficiently construct the hierarchy during traversal. Furthermore, we introduce a new idea to avoid traversal. We construct the subgraphs while visiting neighborhoods in the peeling process, and build the relations to previously constructed subgraphs. We also consider an existing idea to find the k-core hierarchy and adapt for our objectives efficiently. Experiments on different types of large scale real-world networks show significant speedups over naive algorithms and existing alternatives. Our algorithms also outperform the hypothetical limits of any possible traversal-based solution. | Fast Hierarchy Construction for Dense Subgraphs | 9,246 |
In large-scale emergencies social media has become a key source of information for public awareness, government authorities and relief agencies. However, the sheer volume of data and the low signal-to- noise ratio limit the effectiveness and the efficiency of using social media as an intelligence resource. We describe Australian Crisis Tracker (ACT), a tool designed for agencies responding to large- scale emergency events, to facilitate the understanding of critical information in Twitter. ACT was piloted by the Australian Red Cross (ARC) during the 2013-2014 Australian bushfires season. Video is available at: https://www.youtube.com/watch?v=Y-1rtNFqQbE | Project ACT: Social Media Analytics in Disaster Response | 9,247 |
For many European countries, in 2015 the refugee situation developed from a remote tragedy reported upon in the news to a situation they have to deal with in their own neighborhood. Driven by this observation, we investigated the development of the perception of the refugee situation during 2015 in Twitter. Starting from a dataset of 1.7 Million tweets covering refugee-related topics from May to December 2015, we investigated how the discussion on refugees changed over time, in different countries as well as in relationship with the evolution of the actual situation. In this paper we report and discuss our findings from checking a set of hypotheses, such as that the closeness to the actual situation would influence the intensity and polarity of discussions and that news media takes a mediating role between the actual and perceived refugee situation. | Welcome or Not-Welcome: Reactions to Refugee Situation on Social Media | 9,248 |
There are several metrics (Modularity, Mutual Information, Conductance, etc.) to evaluate the strength of graph clustering in large graphs. These metrics have great significance to measure the effectiveness and they are often used to find the strongly connected clusters with respect to the whole graph. In this paper, we propose a new metric to evaluate the strength of graph clustering and also study its applications. We show that our proposed metric has great consistency which is similar to other metrics and easy to calculate. Our proposed metric also shows consistency where other metrics fail in some special cases. We demonstrate that our metric has reasonable strength while extracting strongly connected communities in both simulated (in silico) data and real data networks. We also show some comparative results of our proposed metric with other popular metric(s) for Online Social Networks (OSN) and Gene Regulatory Networks (GRN). | NEDindex: A new metric for community structure in networks | 9,249 |
Online social networks being extended to geographical space has resulted in large amount of user check-in data. Understanding check-ins can help to build appealing applications, such as location recommendation. In this paper, we propose DeepCity, a feature learning framework based on deep learning, to profile users and locations, with respect to user demographic and location category prediction. Both of the predictions are essential for social network companies to increase user engagement. The key contribution of DeepCity is the proposal of task-specific random walk which uses the location and user properties to guide the feature learning to be specific to each prediction task. Experiments conducted on 42M check-ins in three cities collected from Instagram have shown that DeepCity achieves a superior performance and outperforms other baseline models significantly. | DeepCity: A Feature Learning Framework for Mining Location Check-ins | 9,250 |
As the number and variety of smart devices increase, users may use myriad devices in their daily lives and the online activities become highly fragmented. Building an accurate user identity becomes a difficult and important problem for advertising companies. The task for the CIKM Cup 2016 Track 1 was to find the same user cross multiple devices. This paper discusses our solution to the challenge. It is mainly comprised of three parts: comprehensive feature engineering, negative sampling, and model selection. For each part we describe our special steps and demonstrate how the performance is boosted. We took the second prize of the competition with an F1-score of 0.41669. | Cross-Device User Matching Based on Massive Browse Logs: The Runner-Up
Solution for the 2016 CIKM Cup | 9,251 |
The paper deals with fractal characteristics (Hurst exponent) and wavelet-scaleograms of the information distribution model, suggested by the authors. The authors have studied the effect of Hurst exponent change depending upon the model parameters, which have semantic meaning. The paper also considers fractal characteristics of real information streams. It is described, how the Hurst exponent dynamics depends on these information streams state in practice | Fractal Properties of Multiagent News Diffusion Model | 9,252 |
The rating score prediction is widely studied in recommender system, which predicts the rating scores of users on items through making use of the user-item interaction information. Besides the rating information between users and items, lots of additional information have been employed to promote recommendations, such as social relation and geographic location. Expenditure information on each transaction between users and items is widely available on e-commerce websites, often appearing next to the rating information, while there is seldom study on the correlation between expenditures and rating scores. In this paper, we first study their correlations in real data sets and propose the expenditure aware rating prediction problem. From the data sets crawled from a well-known social media platform Dianping in China, we find some insightful correlations between expenditures and rating scores: 1) transactions or experiences with higher expenditures usually lead to higher rating scores; 2) when the real expenditures are higher than users' normal spending behavior, the users usually give higher scores; and 3) there are multiple grades of expenditure behaviors. Based on these three observations, we propose an Expenditure ware RatingPrediction method (EARP), based on low-rank matrix factorization, to effectively incorporate the expenditure information. Extensive experiments on five real data sets show that EARP not only always outperforms other state-of-the-art baselines but also discovers the latent characteristics of users and businesses. | Expenditure Aware Rating Prediction for Recommendation | 9,253 |
Broadcasts and timelines are the primary mechanism of information exchange in online social platforms today. Services like Facebook, Twitter and Instagram have enabled ordinary people to reach large audiences spanning cultures and countries, while their massive popularity has created increasingly competitive marketplaces of attention. Timing broadcasts to capture the attention of such geographically diverse audiences has sparked interest from many startups and social marketing gurus. However, formal study is lacking on both the timing and frequency problems. We study for the first time the broadcast scheduling problem of specifying the timing and frequency of publishing content to maximise the attention received. We validate and quantify three interacting behavioural phenomena to parametrise social platform users: information overload, bursty circadian rhythms and monotony aversion, which is defined here for the first time. We formalise a timeline information exchange process based on these phenomena, and formulate an objective function that quantifies the expected collective attention. We finally present experiments on real data from Twitter, where we discover a counter-intuitive scheduling strategy that outperforms popular heuristics while producing fewer posts. | Scheduling Broadcasts in a Network of Timelines | 9,254 |
Nowadays users get informed and shape their opinion through social media. However, the disintermediated access to contents does not guarantee quality of information. Selective exposure and confirmation bias, indeed, have been shown to play a pivotal role in content consumption and information spreading. Users tend to select information adhering (and reinforcing) their worldview and to ignore dissenting information. This pattern elicits the formation of polarized groups -- i.e., echo chambers -- where the interaction with like-minded people might even reinforce polarization. In this work we address news consumption around Brexit in UK on Facebook. In particular, we perform a massive analysis on more than 1 Million users interacting with Brexit related posts from the main news providers between January and July 2016. We show that consumption patterns elicit the emergence of two distinct communities of news outlets. Furthermore, to better characterize inner group dynamics, we introduce a new technique which combines automatic topic extraction and sentiment analysis. We compare how the same topics are presented on posts and the related emotional response on comments finding significant differences in both echo chambers and that polarization influences the perception of topics. Our results provide important insights about the determinants of polarization and evolution of core narratives on online debating. | The Anatomy of Brexit Debate on Facebook | 9,255 |
In recent years, a vast amount of research has been conducted on learning people's interests from their actions. Yet their collective actions also allow us to learn something about the world, in particular, infer attributes of places people visit or interact with. Imagine classifying whether a hotel has a gym or a swimming pool, or whether a restaurant has a romantic atmosphere without ever asking its patrons. Algorithms we present can do just that. Many web applications rely on knowing attributes of places, for instance, whether a particular restaurant has WiFi or offers outdoor seating. Such data can be used to support a range of user experiences, from explicit query-driven search to personalized place recommendations. However, obtaining these attributes is generally difficult, with existing approaches relying on crowdsourcing or parsing online reviews, both of which are noisy, biased, and have limited coverage. Here we present a novel approach to classifying place attributes, which learns from patrons' visit patterns based on anonymous observational data. Our method, STEPS, learns from aggregated sequences of place visits. For example, if many people visit the restaurant on a Saturday evening, coming from a luxury hotel or theater, and stay for a long time, then this restaurant is more likely to have a romantic atmosphere. On the other hand, if most people visit the restaurant on weekdays, coming from work or a grocery store, then the restaurant is less likely to be romantic. We show that such transition features are highly predictive of place attributes. In an extensive empirical evaluation, STEPS nearly doubled the coverage of a state of the art approach thanks to learning from observational location data, which allowed our method to reason about many more places. | STEPS: Predicting place attributes via spatio-temporal analysis | 9,256 |
Online Social Networks explode with activity whenever a crisis event takes place. Most content generated as part of this activity is a mixture of text and images, and is particularly useful for first responders to identify popular topics of interest and gauge the pulse and sentiment of citizens. While multiple researchers have used text to identify, analyze and measure themes and public sentiment during such events, little work has explored visual themes floating on networks in the form of images, and the sentiment inspired by them. Given the potential of visual content for influencing users' thoughts and emotions, we perform a large scale analysis to compare popular themes and sentiment across images and textual content posted on Facebook during the terror attacks that took place in Paris in 2015. Using state-of-the-art image summarization techniques, we discovered multiple visual themes which were popular in images, but were not identifiable through text. We uncovered instances of misinformation and false flag (conspiracy) theories among popular image themes, which were not prominent in user generated textual content, and can be of particular inter- est to first responders. Our analysis also revealed that while textual content posted after the attacks reflected negative sentiment, images inspired positive sentiment. To the best of our knowledge, this is the first large scale study of images posted on social networks during a crisis event. | Visual Themes and Sentiment on Social Networks To Aid First Responders
During Crisis Events | 9,257 |
Pok\'emon Go, a location-based game that uses augmented reality techniques, received unprecedented media coverage due to claims that it allowed for greater access to public spaces, increasing the number of people out on the streets, and generally improving health, social, and security indices. However, the true impact of Pok\'emon Go on people's mobility patterns in a city is still largely unknown. In this paper, we perform a natural experiment using data from mobile phone networks to evaluate the effect of Pok\'emon Go on the pulse of a big city: Santiago, capital of Chile. We found significant effects of the game on the floating population of Santiago compared to movement prior to the game's release in August 2016: in the following week, up to 13.8\% more people spent time outside at certain times of the day, even if they do not seem to go out of their usual way. These effects were found by performing regressions using count models over the states of the cellphone network during each day under study. The models used controlled for land use, daily patterns, and points of interest in the city. Our results indicate that, on business days, there are more people on the street at commuting times, meaning that people did not change their daily routines but slightly adapted them to play the game. Conversely, on Saturday and Sunday night, people indeed went out to play, but favored places close to where they live. Even if the statistical effects of the game do not reflect the massive change in mobility behavior portrayed by the media, at least in terms of expanse, they do show how "the street" may become a new place of leisure. This change should have an impact on long-term infrastructure investment by city officials, and on the drafting of public policies aimed at stimulating pedestrian traffic. | The Effect of Pokémon Go on The Pulse of the City: A Natural
Experiment | 9,258 |
Digital traces of conversations in micro-blogging platforms and OSNs provide information about user opinion with a high degree of resolution. These information sources can be exploited to under- stand and monitor collective behaviors. In this work, we focus on polarization classes, i.e., those topics that require the user to side exclusively with one position. The proposed method provides an iterative classification of users and keywords: first, polarized users are identified, then polarized keywords are discovered by monitoring the activities of previously classified users. This method thus allows tracking users and topics over time. We report several experiments conducted on two Twitter datasets during political election time-frames. We measure the user classification accuracy on a golden set of users, and analyze the relevance of the extracted keywords for the ongoing political discussion. | Polarized User and Topic Tracking in Twitter | 9,259 |
Psychologists have demonstrated that pets have a positive impact on owners' happiness. For example, lonely people are often advised to have a dog or cat to quell their social isolation. Conventional psychological research methods of analyzing this phenomenon are mostly based on surveys or self-reported questionnaires, which are time-consuming and lack of scalability. Utilizing social media as an alternative and complimentary resource could potentially address both issues and provide different perspectives on this psychological investigation. In this paper, we propose a novel and effective approach that exploits social media to study the effect of pets on owners' happiness. The proposed framework includes three major components: 1) collecting user-level data from Instagram consisting of about 300,000 images from 2905 users; 2) constructing a convolutional neural network (CNN) for pets classification, and combined with timeline information, further identifying pet owners and the control group; 3) measuring the confidence score of happiness by detecting and analyzing selfie images. Furthermore, various factors of demographics are employed to analyze the fine-grained effects of pets on happiness. Our experimental results demonstrate the effectiveness of the proposed approach and we believe that this approach can be applied to other related domains as a large-scale, high-confidence methodology of user activity analysis through social media. | The Effect of Pets on Happiness: A Data-Driven Approach via Large-Scale
Social Media | 9,260 |
Live online social broadcasting services like YouTube Live and Twitch have steadily gained popularity due to improved bandwidth, ease of generating content and the ability to earn revenue on the generated content. In contrast to traditional cable television, revenue in online services is generated solely through advertisements, and depends on the number of clicks generated. Channel owners aim to opportunistically schedule advertisements so as to generate maximum revenue. This paper considers the problem of optimal scheduling of advertisements in live online social media. The problem is formulated as a multiple stopping problem and is addressed in a partially observed Markov decision process (POMDP) framework. Structural results are provided on the optimal advertisement scheduling policy. By exploiting the structure of the optimal policy, best linear thresholds are computed using stochastic approximation. The proposed model and framework are validated on real datasets, and the following observations are made: (i) The policy obtained by the multiple stopping problem can be used to detect changes in ground truth from online search data (ii) Numerical results show a significant improvement in the expected revenue by opportunistically scheduling the advertisements. The revenue can be improved by $20-30\%$ in comparison to currently employed periodic scheduling. | Opportunistic Advertisement Scheduling in Live Social Media: A Multiple
Stopping Time POMDP Approach | 9,261 |
YouTube, with millions of content creators, has become the preferred destination for watching videos online. Through the Partner program, YouTube allows content creators to monetize their popular videos. Of significant importance for content creators is which meta-level features (e.g. title, tag, thumbnail) are most sensitive for promoting video popularity. The popularity of videos also depends on the social dynamics, i.e. the interaction of the content creators (or channels) with YouTube users. Using real-world data consisting of about 6 million videos spread over 25 thousand channels, we empirically examine the sensitivity of YouTube meta-level features and social dynamics. The key meta-level features that impact the view counts of a video include: first day view count , number of subscribers, contrast of the video thumbnail, Google hits, number of keywords, video category, title length, and number of upper-case letters in the title respectively and illustrate that these meta-level features can be used to estimate the popularity of a video. In addition, optimizing the meta-level features after a video is posted increases the popularity of videos. In the context of social dynamics, we discover that there is a causal relationship between views to a channel and the associated number of subscribers. Additionally, insights into the effects of scheduling and video playthrough in a channel are also provided. Our findings provide a useful understanding of user engagement in YouTube. | Engagement dynamics and sensitivity analysis of YouTube videos | 9,262 |
This paper introduces new techniques for sampling attributed networks to support standard Data Mining tasks. The problem is important for two reasons. First, it is commonplace to perform data mining tasks such as clustering and classification of network attributes (attributes of the nodes, including social media posts). Furthermore, the extraordinarily large size of real-world networks necessitates that we work with a smaller graph sample. Second, while random sampling will provide an unbiased estimate of content, random access is often unavailable for many networks. Hence, network samplers such as Snowball sampling, Forest Fire, Random Walk, Metropolis-Hastings Random Walk are widely used; however, these attribute-agnostic samplers were designed to capture salient properties of network structure, not node content. The latter is critical for clustering and classification tasks. There are three contributions of this paper. First, we introduce several attribute-aware samplers based on Information Theoretic principles. Second, we prove that these samplers have a bias towards capturing new content, and are equivalent to uniform sampling in the limit. Finally, our experimental results over large real-world datasets and synthetic benchmarks are insightful: attribute-aware samplers outperform both random sampling and baseline attribute-agnostic samplers by a wide margin in clustering and classification tasks. | Task-driven sampling of attributed networks | 9,263 |
Understanding the process by which a contagion disseminates throughout a network is of great importance in many real world applications. The required sophistication of the inference approach depends on the type of information we want to extract as well as the number of observations that are available to us. We analyze scenarios in which not only the underlying network structure (parental relationships and link strengths) needs to be detected, but also the infection times must be estimated. We assume that our only observation of the diffusion process is a set of time series, one for each node of the network, which exhibit changepoints when an infection occurs. After formulating a model to describe the contagion, and selecting appropriate prior distributions, we seek to find the set of model parameters that best explains our observations. Modeling the problem in a Bayesian framework, we exploit Monte Carlo Markov Chain, Sequential Monte Carlo, and time series analysis techniques to develop batch and online inference algorithms. We evaluate the performance of our proposed algorithms via numerical simulations of synthetic network contagions and analysis of real-world datasets. | Online Bayesian Inference of Diffusion Networks | 9,264 |
How can we design a product or movie that will attract, for example, the interest of Pennsylvania adolescents or liberal newspaper critics? What should be the genre of that movie and who should be in the cast? In this work, we seek to identify how we can design new movies with features tailored to a specific user population. We formulate the movie design as an optimization problem over the inference of user-feature scores and selection of the features that maximize the number of attracted users. Our approach, PNP, is based on a heterogeneous, tripartite graph of users, movies and features (e.g., actors, directors, genres), where users rate movies and features contribute to movies. We learn the preferences by leveraging user similarities defined through different types of relations, and show that our method outperforms state-of-the-art approaches, including matrix factorization and other heterogeneous graph-based analysis. We evaluate PNP on publicly available real-world data and show that it is highly scalable and effectively provides movie designs oriented towards different groups of users, including men, women, and adolescents. | PNP: Fast Path Ensemble Method for Movie Design | 9,265 |
Gender is playing an important role in the 2016 U.S. presidential election, especially with Hillary Clinton becoming the first female presidential nominee and Donald Trump being frequently accused of sexism. In this paper, we introduce computer vision to the study of gender politics and present an image-driven method that can measure the effects of gender in an accurate and timely manner. We first collect all the profile images of the candidates' Twitter followers. Then we train a convolutional neural network using images that contain gender labels. Lastly, we classify all the follower and unfollower images. Through two case studies, one on the `woman card' controversy and one on Sanders followers, we demonstrate how gender is informing the 2016 presidential election. Our framework of analysis can be readily generalized to other case studies and elections. | Gender Politics in the 2016 U.S. Presidential Election: A Computer
Vision Approach | 9,266 |
Given a social network of experts, we address the problem of discovering a team of experts that collectively holds a set of skills required to complete a given project. Most prior work ranks possible solutions by communication cost, represented by edge weights in the expert network. Our contribution is to take experts authority into account, represented by node weights. We formulate several problems that combine communication cost and authority, we prove that they are NP-hard, and we propose and experimentally evaluate greedy algorithms to solve them. | Authority-based Team Discovery in Social Networks | 9,267 |
In this paper, we propose a web-centered framework to infer voter preferences for the 2016 U.S. presidential primaries. Using Twitter data collected from Sept. 2015 to March 2016, we first uncover the tweeting tactics of the candidates and then exploit the variations in the number of 'likes' to infer voters' preference. With sparse learning, we are able to reveal neutral topics as well as positive and negative ones. Methodologically, we are able to achieve a higher predictive power with sparse learning. Substantively, we show that for Hillary Clinton the (only) positive issue area is women's rights. We demonstrate that Hillary Clinton's tactic of linking herself to President Obama resonates well with her supporters but the same is not true for Bernie Sanders. In addition, we show that Donald Trump is a major topic for all the other candidates, and that the women's rights issue is equally emphasized in Sanders' campaign as in Clinton's. | Tactics and Tallies: Inferring Voter Preferences in the 2016 U.S.
Presidential Primaries Using Sparse Learning | 9,268 |
The last two decades have seen a tremendous surge in research on social networks and their implications. The studies includes inferring social relationships, which in turn have been used for target advertising, recommendations, search customization etc. However, the offline experiences of human, the conversations with people and face-to-face interactions that govern our lives interactions have received lesser attention. We introduce DAIICT Spatio-Temporal Network (DSSN), a spatiotemporal dataset of 0.7 million data points of continuous location data logged at an interval of every 2 minutes by mobile phones of 46 subjects. Our research is focused at inferring relationship strength between students based on the spatiotemporal data and comparing the results with the self-reported data. In that pursuit we introduce Temporal Diversity, which we show to be superior in its contribution to predicting relationship strength than its counterparts. We also explore the evolving nature of Temporal Diversity with time. Our rich dataset opens various other avenues of research that require fine-grained location data with bounded movement of participants within a limited geographical area. The advantage of having a bounded geographical area such as a university campus is that it provides us with a microcosm of the real world, where each such geographic zone has an internal context and function and a high percentage of mobility is governed by schedules and time-tables. The bounded geographical region in addition to the age homogeneous population gives us a minute look into the active internal socialization of students in a university. | Role of Temporal Diversity in Inferring Social Ties Based on
Spatio-Temporal Data | 9,269 |
Online social network analysis has attracted great attention with a vast number of users sharing information and availability of APIs that help to crawl online social network data. In this paper, we study the research studies that are helpful for user characterization as online users may not always reveal their true identity or attributes. We especially focused on user attribute determination such as gender, age, etc.; user behavior analysis such as motives for deception; mental models that are indicators of user behavior; user categorization such as bots vs. humans; and entity matching on different social networks. We believe our summary of analysis of user characterization will provide important insights to researchers and better services to online users. | User characterization for online social networks | 9,270 |
Understanding the information behind social relationships represented by a network is very challenging, especially, when the social interactions change over time inducing updates on the network topology. In this context, this paper proposes an approach for analysing dynamic social networks, more precisely for Twitter's network. Our approach relies on two complementary steps: (i) an online community identification based on a dynamic community detection algorithm called Dyci. The main idea of Dyci is to track whether a connected component of the weighted graph becomes weak over time, in order to merge it with the "dominant" neighbour community. Additionally, (ii) a community visualization is provided by our visualization tool called NLCOMS, which combines between two methods of dynamic network visualization. In order to assess the efficiency and the applicability of the proposed approach, we consider real-world data of the ANR-Info-RSN project, which deals with community analysis in Twitter. | On the visualization of the detected communities in dynamic networks: A
case study of Twitter's network | 9,271 |
We analyze the problem of majority sentiment detection in Online Social Networks (OSN), and relate the detection error probability to the underlying graph of the OSN. Modeling the underlying social network as an Ising Markov random field prior based on a given graph, we show that in the case of the empty graph (independent sentiments) and the chain graph, the detection is always inaccurate, even when the number of users grow to infinity. In the case of the complete graph, the detection is inaccurate if the connection strength is below a certain critical value, while it is asymptotically accurate if the strength is above that critical value, which is analogous to the phase transition phenomenon in statistical physics. | Asymptotic Performance Analysis of Majority Sentiment Detection in
Online Social Networks | 9,272 |
Intimate partner violence (IPV) is a significant public health problem and social issue that involves couples from all socioeconomic and cultural contexts. IPV may affect women and men, but these latter are the most common perpetrators of IPV. We developed stochastic Agent-Based models of IPV focused on the couple dynamics, determined by the parallel, individual behaviour of partners. Based on the psychological theory of the Cycle of Violence, we have developed a model based on four discrete states: passivity, normal situation, upset and physical assault. The individual transition probability depends on the previous state of the subject and that of the partner, and on a control parameter, the aggressiveness. We then let this parameter evolve depending on the perceived violence from past experiences (polarisation) or from the support received from the environment (social influence). From the analysis of the phase diagrams we observe the emergence of characteristic patterns, in agreement with the observations of IPV in the literature. | Stochastic Agent-Based Models of Intimate Partner Violence | 9,273 |
Online reviews play a crucial role in helping consumers evaluate and compare products and services. However, review hosting sites are often targeted by opinion spamming. In recent years, many such sites have put a great deal of effort in building effective review filtering systems to detect fake reviews and to block malicious accounts. Thus, fraudsters or spammers now turn to compromise, purchase or even raise reputable accounts to write fake reviews. Based on the analysis of a real-life dataset from a review hosting site (dianping.com), we discovered that reviewers' posting rates are bimodal and the transitions between different states can be utilized to differentiate spammers from genuine reviewers. Inspired by these findings, we propose a two-mode Labeled Hidden Markov Model to detect spammers. Experimental results show that our model significantly outperforms supervised learning using linguistic and behavioral features in identifying spammers. Furthermore, we found that when a product has a burst of reviews, many spammers are likely to be actively involved in writing reviews to the product as well as to many other products. We then propose a novel co-bursting network for detecting spammer groups. The co-bursting network enables us to produce more accurate spammer groups than the current state-of-the-art reviewer-product (co-reviewing) network. | Modeling Review Spam Using Temporal Patterns and Co-bursting Behaviors | 9,274 |
Online Social Networks (OSNs), such as Facebook, provide users with tools to share information along with a set of privacy controls preferences to regulate the spread of information. Current privacy controls are efficient to protect content data. However, the complexity of tuning them undermine their efficiency when protecting contextual information (such as the social network structure) that many users believe being kept private. In this paper, we demonstrate the extent of the problem of information leakage in Facebook. In particular, we show the possibility of inferring, from the network "surrounding" a victim user, some information that the victim set as hidden. We developed a system, named OSSINT (Open Source Social Network INTelligence), on top of our previous tool SocialSpy, that is able to infer hidden information of a victim profile and retrieve private information from public one. OSSINT retrieves the friendship network of a victim and shows how it is possible to infer additional private information (e.g., user personal preferences and hobbies). Our proposed system OSSINT goes extra mile about the network topology information, i.e., predicting new friendships using the victim's friends of friends network (2-hop of distance from the victim profile), and hence possibly deduce private information of the full Facebook network. OSSINT correctly improved the previous results of SocialSpy predicting an average of 11 additional friendships with peaks of 20 new friends. Moreover, OSSINT, for the considered victim profiles demonstrated how it is possible to infer real life information such as current city, hometown, university, supposed being private. | OSSINT - Open Source Social Network Intelligence An efficient and
effective way to uncover "private" information in OSN profiles | 9,275 |
Censorship in social media has been well studied and provides insight into how governments stifle freedom of expression online. Comparatively less (or no) attention has been paid to detecting (self) censorship in traditional media (e.g., news) using social media as a bellweather. We present a novel unsupervised approach that views social media as a sensor to detect censorship in news media wherein statistically significant differences between information published in the news media and the correlated information published in social media are automatically identified as candidate censored events. We develop a hypothesis testing framework to identify and evaluate censored clusters of keywords, and a new near-linear-time algorithm (called GraphDPD) to identify the highest scoring clusters as indicators of censorship. We outline extensive experiments on semi-synthetic data as well as real datasets (with Twitter and local news media) from Mexico and Venezuela, highlighting the capability to accurately detect real-world self censorship events. | Can Self-Censorship in News Media be Detected Algorithmically? A Case
Study in Latin America | 9,276 |
This paper studies the problem of locating multiple diffusion sources in networks with partial observations. We propose a new source localization algorithm, named Optimal-Jordan-Cover (OJC). The algorithm first extracts a subgraph using a candidate selection algorithm that selects source candidates based on the number of observed infected nodes in their neighborhoods. Then, in the extracted subgraph, OJC finds a set of nodes that "cover" all observed infected nodes with the minimum radius. The set of nodes is called the Jordan cover, and is regarded as the set of diffusion sources. Considering the heterogeneous susceptible-infected-recovered (SIR) diffusion in the Erdos-Renyi (ER) random graph, we prove that OJC can locate all sources with probability one asymptotically with partial observations. OJC is a polynomial-time algorithm in terms of network size. However, the computational complexity increases exponentially in $m,$ the number of sources. We further propose a low-complexity heuristic based on the K-Means for approximating the Jordan cover, named Approximate-Jordan-Cover (AJC). Simulations on random graphs and real networks demonstrate that both AJC and OJC significantly outperform other heuristic algorithms. | Catch'Em All: Locating Multiple Diffusion Sources in Networks with
Partial Observations | 9,277 |
According to NSDUH (National Survey on Drug Use and Health), 20 million Americans consumed drugs in the past few 30 days. Combating illicit drug use is of great interest to public health and law enforcement agencies. Despite of the importance, most of the existing studies on drug uses rely on surveys. Surveys on sensitive topics such as drug use may not be answered truthfully by the people taking them. Selecting a representative sample to survey is another major challenge. In this paper, we explore the possibility of using big multimedia data, including both images and text, from social media in order to discover drug use patterns at fine granularity with respect to demographics. Instagram posts are searched and collected by drug related terms by analyzing the hashtags supplied with each post. A large and dynamic dictionary of frequent drug related slangs is used to find these posts. User demographics are extracted using robust face image analysis algorithms. These posts are then mined to find common trends with regard to the time and location they are posted, and further in terms of age and gender of the drug users. Furthermore, by studying the accounts followed by the users of drug related posts, we extract common interests shared by drug users. | Fine-grained Mining of Illicit Drug Use Patterns Using Social Multimedia
Data from Instagram | 9,278 |
The detection of influential nodes in a social network is an active research area with many valuable applications including marketing and advertisement. As a new application in academia, KDD Cup 2016 shed light on the lack of an existing objective ranking for institutions within their respective research areas and proposed a solution for it. In this problem, the academic fields are defined as social networks whose nodes are the active institutions within the field, with the most influential nodes representing the highest contributors. The solution is able to provide a ranking of active institutions within their specific domains. The problem statement provided an annual scoring mechanism for institutions based on their publications and encouraged the use of any publicly available dataset such as the Microsoft Academic Graph (MAG). The contest was focused on research publications in selected conferences and asked for a prediction of the ranking for active institutions within those conferences in 2016. It should be noted that the results of the paper submissions and therefore the ground truths for KDD Cup were unknown at the time of the contest. Each team's final ranking list was evaluated by a metric called NDCG@20 after the results were released. This metric was used to indicate the distance between each team's proposed ranking and the actual one once it was known. After computing the scores of institutions for each year starting from 2011, we aggregated the rankings by summing the normalized scores across the years and using the final score set to provide the final ranking. Since the 2016 ground truths were unknown, we utilized the scores from 2011-2014 and used the 2015 publications as a test bed for evaluating our aggregation method. Based on the testing, summing the normalized scores got us closest to the actual 2015 rankings and using same heuristic for predicting the 2016 results. | Ranking Research Institutions Based On Related Academic Conferences | 9,279 |
How information spreads through a social network? Can we assume, that the information is spread only through a given social network graph? What is the correct way to compare the models of information flow? These are the basic questions we address in this work. We focus on meticulous comparison of various, well-known models of rumor propagation in the social network. We introduce the model incorporating mass media and effects of absent nodes. In this model the information appears spontaneously in the graph. Using the most conservative metric, we showed that the distribution of cascades sizes generated by this model fits the real data much better than the previously considered models. | There is Something Beyond the Twitter Network | 9,280 |
With the growing use of popular social media services like Facebook and Twitter it is challenging to collect all content from the networks without access to the core infrastructure or paying for it. Thus, if all content cannot be collected one must consider which data are of most importance. In this work we present a novel User-guided Social Media Crawling method (USMC) that is able to collect data from social media, utilizing the wisdom of the crowd to decide the order in which user generated content should be collected to cover as many user interactions as possible. USMC is validated by crawling 160 public Facebook pages, containing content from 368 million users including 1.3 billion interactions, and it is compared with two other crawling methods. The results show that it is possible to cover approximately 75% of the interactions on a Facebook page by sampling just 20% of its posts, and at the same time reduce the crawling time by 53%. In addition, the social network constructed from the 20% sample contains more than 75% of the users and edges compared to the social network created from all posts, and it has similar degree distribution. | Do we really need to catch them all? A new User-guided Social Media
Crawling method | 9,281 |
In a social network, influence diffusion is the process of spreading innovations from user to user. An activation state identifies who are the active users who have adopted the target innovation. Given an activation state of a certain diffusion, effector detection aims to reveal the active users who are able to best explain the observed state. In this paper, we tackle the effector detection problem from two perspectives. The first approach is based on the influence distance that measures the chance that an active user can activate its neighbors. For a certain pair of users, the shorter the influence distance, the higher probability that one can activate the other. Given an activation state, the effectors are expected to have short influence distance to active users while long to inactive users. By this idea, we propose the influence-distance-based effector detection problem and provide a 3-approximation. Second, we address the effector detection problem by the maximum likelihood estimation (MLE) approach. We prove that the optimal MLE can be obtained in polynomial time for connected directed acyclic graphs. For general graphs, we first extract a directed acyclic subgraph that can well preserve the information in the original graph and then apply the MLE approach to the extracted subgraph to obtain the effectors. The effectiveness of our algorithms is experimentally verified via simulations on the real-world social network. | Effector Detection in Social Networks | 9,282 |
The term filter bubble has been coined to describe the situation of online users which---due to filtering algorithms---live in a personalised information universe biased towards their own interests.In this paper we use an agent-based simulation framework to measure the actual risk and impact of filter bubble effects occurring in online communities due to content or author based personalisation algorithms. Observing the strength of filter bubble effects allows for opposing the benefits to the risks of personalisation.In our simulation we observed, that filter bubble effects occur as soon as users indicate preferences towards certain topics.We also saw, that well connected users are affected much stronger than average or poorly connected users. Finally, our experimental setting indicated that the employed personalisation algorithm based on content features seems to bear a lower risk of filter bubble effects than one performing personalisation based on authors. | The Impact of the Filter Bubble -- A Simulation Based Framework for
Measuring Personalisation Macro Effects in Online Communities | 9,283 |
This paper proposes a new graph proximity measure. This measure is a derivative of network reliability. By analyzing its properties and comparing it against other proximity measures through graph examples, we demonstrate that it is more consistent with human intuition than competitors. A new deterministic algorithm is developed to approximate this measure with practical complexity. Empirical evaluation by two link prediction benchmarks, one in coauthorship networks and one in Wikipedia, shows promising results. For example, a single parameterization of this measure achieves accuracies that are 14-35% above the best accuracy for each graph of all predictors reported in the 2007 Liben-Nowell and Kleinberg survey. | A Proximity Measure using Blink Model | 9,284 |
The problem of ideology detection is to study the latent (political) placement for people, which is traditionally studied on politicians according to their voting behaviors. Recently, more and more studies begin to address the ideology detection problem for ordinary users based on their online behaviors that can be captured by social media, e.g., Twitter. As far as we are concerned, however, the vast majority of the existing methods on ideology detection on social media have oversimplified the problem as a binary classification problem (i.e., liberal vs. conservative). Moreover, though social links can play a critical role in deciding one's ideology, most of the existing work ignores the heterogeneous types of links in social media. In this paper we propose to detect \emph{numerical} ideology positions for Twitter users, according to their \emph{follow}, \emph{mention}, and \emph{retweet} links to a selected set of politicians. A unified probabilistic model is proposed that can (1) explain the reasons why links are built among people in terms of their ideology, (2) integrate heterogeneous types of links together in determining people's ideology, and (3) automatically learn the quality of each type of links in deciding one's ideology. Experiments have demonstrated the advantages of our model in terms of both ranking and political leaning classification accuracy. It is shown that (1) using multiple types of links is better than using any single type of links alone to determine one's ideology, and (2) our model is even more superior than baselines when dealing with people that are sparsely linked in one type of links. We also show that the detected ideology for Twitter users aligns with our intuition quite well. | Ideology Detection for Twitter Users with Heterogeneous Types of Links | 9,285 |
Smartphones have become extremely popular by launching wide ubiquitous networks. Nowadays studying of DTN Delay Tolerant Networks (DTN) and Opportunistic Networks where formed over these mobile nodes, is one of the interesting topics in the research community. In this paper, we measure communicability capacity of DTN Network formed over the mobile nodes at a university campus and also an area in Montreal city through exploiting static and temporal graphs. We observed a significant difference between communicability measures in static and temporal cases, especially for short snapshot windows. It implies that analyzing dynamic networks by considering a static model for them may lead to an unrealistic and even mislead results. | Characterizing Communicability of Networks formed on Mobile Nodes | 9,286 |
This paper studies marijuana-related tweets in social network Twitter. We collected more than 300,000 marijuana related tweets during November 2016 in our study. Our text-mining based algorithms and data analysis unveil some interesting patterns including: (i) users' attitudes (e.g., positive or negative) can be characterized by the existence of outer links in a tweet; (ii) 67% users use their mobile phones to post their messages while many users publish their messages using third-party automatic posting services; and (3) the number of tweets during weekends is much higher than during weekdays. Our data also showed the impact of the political events such as the U.S. presidential election or state marijuana legalization votes on the marijuana-related tweeting frequencies. | Evaluating Marijuana-Related Tweets On Twitter | 9,287 |
The study of networks has emerged in diverse disciplines as a means of analyzing complex relationship data. Beyond graph analysis tasks like graph query processing, link analysis, influence propagation, there has recently been some work in the area of outlier detection for information network data. Although various kinds of outliers have been studied for graph data, there is not much work on anomaly detection from edge-attributed graphs. In this paper, we introduce a method that detects novel outlier graph nodes by taking into account the node data and edge data simultaneously to detect anomalies. We model the problem as a community detection task, where outliers form a separate community. We propose a method that uses a probabilistic graph model (Hidden Markov Random Field) for joint modeling of nodes and edges in the network to compute Holistic Community Outliers (HCOutliers). Thus, our model presents a natural setting for heterogeneous graphs that have multiple edges/relationships between two nodes. EM (Expectation Maximization) is used to learn model parameters, and infer hidden community labels. Experimental results on synthetic datasets and the DBLP dataset show the effectiveness of our approach for finding novel outliers from networks. | Community-based Outlier Detection for Edge-attributed Graphs | 9,288 |
Navigation behaviour can be considered as one of the most crucial aspects of user behaviour in an electronic commerce environment, which is very good indicator of user's interests either in the process of browsing or purchasing. Revealing user navigation patterns is very helpful in finding out a way for increasing sale, turning the most browsers into buyers, keeping costumer's attention, loyalty, adjusting and improving the interface in order to boost the user experience and interaction with the system. In this regard, this research has identified the most common user navigation patterns across information networks, illustrated through the example of an electronic bookstore. A behavioural-based model that provides profound knowledge about the processes of navigation is proposed, specifically examined for different types of users, automatically identified and clustered into two clusters according to their navigational behaviour. The developed model is based on stochastic modelling using the concept of Generalized Stochastic Petri Nets which complex solution relies on Continuous Time Markov Chain. As a result, calculation of several performance measures is performed, such as: expected time spent in a transient tangible marking, cumulative sojourn time spent in a transient tangible marking, total number of visits in a transient tangible marking etc. | Behavioural - based modelling and analysis of Navigation Patterns across
Information Networks | 9,289 |
This paper presents a new way to increase interconnectivity in small Wikipedias (fewer than a 100,000 articles), by automatically linking articles based on interlanguage links. Many small Wikipedias have many articles with very few links, this is mainly due to the short article length. This makes it difficult to navigate between the articles. In many cases the article does exist for a small Wikipedia, however the article is just missing a link. Due to the fact that Wikipedias are translated in to many languages, it allows us to generate new links for small Wikipedias using the links from a large Wikipedia (more than a 100,000 articles). | Automatic Wikipedia Link Generation Based On Interlanguage Links | 9,290 |
Social networks allow rapid spread of ideas and innovations while the negative information can also propagate widely. When the cascades with different opinions reaching the same user, the cascade arriving first is the most likely to be taken by the user. Therefore, once misinformation or rumor is detected, a natural containment method is to introduce a positive cascade competing against the rumor. Given a budget $k$, the rumor blocking problem asks for $k$ seed users to trigger the spread of the positive cascade such that the number of the users who are not influenced by rumor can be maximized. The prior works have shown that the rumor blocking problem can be approximated within a factor of $(1-1/e-\delta)$ by a classic greedy algorithm combined with Monte Carlo simulation with the running time of $O(\frac{k^3mn\ln n}{\delta^2})$, where $n$ and $m$ are the number of users and edges, respectively. Unfortunately, the Monte-Carlo-simulation-based methods are extremely time consuming and the existing algorithms either trade performance guarantees for practical efficiency or vice versa. In this paper, we present a randomized algorithm which runs in $O(\frac{km\ln n}{\delta^2})$ expected time and provides a $(1-1/e-\delta)$-approximation with a high probability. The experimentally results on both the real-world and synthetic social networks have shown that the proposed randomized rumor blocking algorithm is much more efficient than the state-of-the-art method and it is able to find the seed nodes which are effective in limiting the spread of rumor. | An Efficient Randomized Algorithm for Rumor Blocking in Online Social
Networks | 9,291 |
It is known that many Twitter users are bots, which are accounts controlled and sometimes created by computers. Twitter bots can send spam tweets, manipulate public opinion and be used for online fraud. Here we report the discovery, retrieval, and analysis of the `Star Wars' botnet in Twitter, which consists of more than 350,000 bots tweeting random quotations exclusively from Star Wars novels. The botnet contains a single type of bot, showing exactly the same properties throughout the botnet. It is unusually large, many times larger than other available datasets. It provides a valuable source of ground truth for research on Twitter bots. We analysed and revealed rich details on how the botnet was designed and created. As of this writing, the Star Wars bots are still alive in Twitter. They have survived since their creation in 2013, despite the increasing efforts in recent years to detect and remove Twitter bots.We also reflect on the `unconventional' way in which we discovered the Star Wars bots, and discuss the current problems and future challenges of Twitter bot detection. | Discovery, Retrieval, and Analysis of 'Star Wars' botnet in Twitter | 9,292 |
The paper presents the graph Fourier transform (GFT) of a signal in terms of its spectral decomposition over the Jordan subspaces of the graph adjacency matrix $A$. This representation is unique and coordinate free, and it leads to unambiguous definition of the spectral components ("harmonics") of a graph signal. This is particularly meaningful when $A$ has repeated eigenvalues, and it is very useful when $A$ is defective or not diagonalizable (as it may be the case with directed graphs). Many real world large sparse graphs have defective adjacency matrices. We present properties of the GFT and show it to satisfy a generalized Parseval inequality and to admit a total variation ordering of the spectral components. We express the GFT in terms of spectral projectors and present an illustrative example for a real world large urban traffic dataset. | Spectral Projector-Based Graph Fourier Transforms | 9,293 |
We propose an inexact method for the graph Fourier transform of a graph signal, as defined by the signal decomposition over the Jordan subspaces of the graph adjacency matrix. This method projects the signal over the generalized eigenspaces of the adjacency matrix, which accelerates the transform computation over large, sparse, and directed adjacency matrices. The trade-off between execution time and fidelity to the original graph structure is discussed. In addition, properties such as a generalized Parseval's identity and total variation ordering of the generalized eigenspaces are discussed. The method is applied to 2010-2013 NYC taxi trip data to identify traffic hotspots on the Manhattan grid. Our results show that identical highly expressed geolocations can be identified with the inexact method and the method based on eigenvector projections, while reducing computation time by a factor of 26,000 and reducing energy dispersal among the spectral components corresponding to the multiple zero eigenvalue. | Agile Inexact Methods for Spectral Projector-Based Graph Fourier
Transforms | 9,294 |
We define and discuss the utility of two equivalence graph classes over which a spectral projector-based graph Fourier transform is equivalent: isomorphic equivalence classes and Jordan equivalence classes. Isomorphic equivalence classes show that the transform is equivalent up to a permutation on the node labels. Jordan equivalence classes permit identical transforms over graphs of nonidentical topologies and allow a basis-invariant characterization of total variation orderings of the spectral components. Methods to exploit these classes to reduce computation time of the transform as well as limitations are discussed. | Graph Equivalence Classes for Spectral Projector-Based Graph Fourier
Transforms | 9,295 |
Recent studies in social media spam and automation provide anecdotal argumentation of the rise of a new generation of spambots, so-called social spambots. Here, for the first time, we extensively study this novel phenomenon on Twitter and we provide quantitative evidence that a paradigm-shift exists in spambot design. First, we measure current Twitter's capabilities of detecting the new social spambots. Later, we assess the human performance in discriminating between genuine accounts, social spambots, and traditional spambots. Then, we benchmark several state-of-the-art techniques proposed by the academic literature. Results show that neither Twitter, nor humans, nor cutting-edge applications are currently capable of accurately detecting the new social spambots. Our results call for new approaches capable of turning the tide in the fight against this raising phenomenon. We conclude by reviewing the latest literature on spambots detection and we highlight an emerging common research trend based on the analysis of collective behaviors. Insights derived from both our extensive experimental campaign and survey shed light on the most promising directions of research and lay the foundations for the arms race against the novel social spambots. Finally, to foster research on this novel phenomenon, we make publicly available to the scientific community all the datasets used in this study. | The paradigm-shift of social spambots: Evidence, theories, and tools for
the arms race | 9,296 |
The increasing popularity of the social networking service, Twitter, has made it more involved in day-to-day communications, strengthening social relationships and information dissemination. Conversations on Twitter are now being explored as indicators within early warning systems to alert of imminent natural disasters such earthquakes and aid prompt emergency responses to crime. Producers are privileged to have limitless access to market perception from consumer comments on social media and microblogs. Targeted advertising can be made more effective based on user profile information such as demography, interests and location. While these applications have proven beneficial, the ability to effectively infer the location of Twitter users has even more immense value. However, accurately identifying where a message originated from or author's location remains a challenge thus essentially driving research in that regard. In this paper, we survey a range of techniques applied to infer the location of Twitter users from inception to state-of-the-art. We find significant improvements over time in the granularity levels and better accuracy with results driven by refinements to algorithms and inclusion of more spatial features. | A survey of location inference techniques on Twitter | 9,297 |
Social feedback has long been recognized as an important element of successful health-related behavior change. However, most of the existing studies look at the effect that offline social feedback has. This paper fills gaps in the literature by proposing a framework to study the causal effect that receiving social support in the form of comments in an online weight loss community has on (i) the probability of the user to return to the forum, and, more importantly, on (ii) the weight loss reported by the user. Using a matching approach for causal inference we observe a difference of 9 lbs lost between users who do or do not receive comments. Surprisingly, this effect is mediated by neither an increase in lifetime in the community nor by an increased activity level of the user. Our results show the importance that a "warm welcome" has when using online support forums to achieve health outcomes. | A Warm Welcome Matters! The Link Between Social Feedback and Weight Loss
in /r/loseit | 9,298 |
Influence maximization is the problem of selecting a set of influential users in the social network. Those users could adopt the product and trigger a large cascade of adoptions through the " word of mouth " effect. In this paper, we propose two evidential influence maximization models for Twitter social network. The proposed approach uses the theory of belief functions to estimate users influence. Furthermore, the proposed influence estimation measure fuses many influence aspects in Twitter, like the importance of the user in the network structure and the popularity of user's tweets (messages). In our experiments, we compare the proposed solutions to existing ones and we show the performance of our models. | Two Evidential Data Based Models for Influence Maximization in Twitter | 9,299 |
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