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ERROR: type should be string, got "https://doi.org/10.1007/s41060 022 00311 6\n\n\n**REVIEW**\n\n\n# The disaster of misinformation: a review of research in social media\n\n**Sadiq Muhammed T[1]** **· Saji K. Mathew[1]**\n\nReceived: 31 May 2021 / Accepted: 6 January 2022 / Published online: 15 February 2022\n© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022\n\n**Abstract**\nThe spread of misinformation in social media has become a severe threat to public interests. For example, several incidents\nof public health concerns arose out of social media misinformation during the COVID-19 pandemic. Against the backdrop of\nthe emerging IS research focus on social media and the impact of misinformation during recent events such as the COVID-19,\nAustralian Bushfire, and the USA elections, we identified disaster, health, and politics as specific domains for a research\nreview on social media misinformation. Following a systematic review process, we chose 28 articles, relevant to the three\nthemes, for synthesis. We discuss the characteristics of misinformation in the three domains, the methodologies that have\nbeen used by researchers, and the theories used to study misinformation. We adapt an Antecedents-Misinformation-Outcomes\n(AMIO) framework for integrating key concepts from prior studies. Based on the AMIO framework, we further discuss the\ninter-relationships of concepts and the strategies to control the spread of misinformation on social media. Ours is one of the\nearly reviews focusing on social media misinformation research, particularly on three socially sensitive domains; disaster,\nhealth, and politics. This review contributes to the emerging body of knowledge in Data Science and social media and informs\nstrategies to combat social media misinformation.\n\n**Keywords Misinformation** Information disorder Social media Systematic literature review\n - - \n\n### 1 Introduction\n\n#### 1.1 Information disorder in social media\n\nRumors, misinformation, disinformation, and malinformation are common challenges confronting media\nof all types. It is, however, worse in the case of digital\nmedia, especially on social media platforms. Ease of access\nand use, speed of information diffusion, and difficulty in\ncorrecting false information make control of undesirable\ninformation a horrid task [1]. Alongside these challenges,\nsocial media has also been highly influential in spreading\ntimely and useful information. For example, the recent\n#BlackLivesMatter movement was enabled by social media,\nwhich united concurring people’s solidarity across the world\nwhen George Floyd was killed due to police brutality, and\nso are 2011 Arab spring in the Middle East and the 2017\n\n### B Sadiq Muhammed T\nsadique.sadiquet@gmail.com\n\nSaji K. Mathew\nsaji@iitm.ac.in\n\n1 Department of Management Studies (DoMS), Indian Institute\nof Technology Madras, Chennai, Tamil Nadu 600036, India\n\n\n#MeToo movement against sexual harassments and abuse [2,\n3]. Although, scholars have addressed information disorder\nin social media, a synthesis of the insights from these studies\nare rare.\nThe information which is fake or misleading and spreads\nunintentionally is known as misinformation [4]. Prior\nresearch on misinformation in social media has highlighted\nvarious characteristics of misinformation and interventions\nthereof in different contexts. The issue of misinformation\nhas become dominant with the rise of social media, attracting\nscholarlyattention,particularlyafterthe2016USAPresidential election, when misinformation apparently influenced the\nelection results [5]. The word ’misinformation’ was listed as\none of the global risks by the World Economic Forum [6]. A\nsimilar term that is popular and confusing along with misinformation is ’disinformation’. It is defined as the information\nthat is fake or misleading, and unlike misinformation, spreads\nintentionally. Disinformation campaigns are often seen in a\npolitical context where state actors create them for political gains. In India, during the initial stage of COVID-19,\nthere was reportedly a surge in fake news linking the virus\noutbreak to a particular religious group. This disinformation spread gained media attention as it was widely shared\n\n## 1 3\n\n\n-----\n\non social media platforms. As a result of the targeting, it\neventually translated into physical violence and discriminatory treatment against members of the community in some\nof the Indian states [7].’Rumors’ and ’fake news’ are similar terms related to misinformation. ’Rumors’ are unverified\ninformation or statements circulated with uncertainty, and\n’fake news’ is the misinformation that is distributed in an official news format. Source ambiguity, personal involvement,\nconfirmation bias, and social ties are some of the rumorcausing factors. Yet another related term, mal-information,\nis accurate information that is used in different contexts to\nspread hatred or abuse of a person or a particular group.\nOur review focuses on misinformation that is spread through\nsocial media platforms. The words ’rumor’, and ’misinformation’ are used interchangeably in this paper. Further, we\nidentify factors that cause misinformation based on a systematic review of prior studies.\nOurs is one of the early attempts to review social media\nresearch on misinformation. This review focuses on three\nsensitive domains of disaster, health, and politics, setting\nthree objectives: (a) to analyze previous studies to understand the impact of misinformation on the three domains\n(b) to identify theoretical perspectives used to examine the\nspread of misinformation on social media and (c) to develop a\nframework to study key concepts and their inter-relationships\nemerging from prior studies. We identified these specific\nareas as the impact of misinformation with regards to both\nspeed of spread and scale of influence are high and detrimental to the public and governments. To the best of our\nknowledge, the review of the literature on social media\nmisinformation themes are relatively scanty. This review\ncontributes to an emerging body of knowledge in Data\nScience and informs the efforts to combat social media misinformation. Data Science is an interdisciplinary area which\nincorporates different areas like statistics, management, and\nsociology to study the data and create knowledge out of data\n\n[8]. This review will also inform future studies that aim to\nevaluate and compare patterns of misinformation on sensitive themes of social relevance, such as disaster, health, and\npolitics.\nThe paper is structured as follows. The first section introduces misinformation in social media context. In Sect. 2,\nwe provide a brief overview of prior research works on\nmisinformation and social media. Section 3 describes the\nresearch methodology, which includes details of the literature search and selection process. Section 4 discusses the\nanalysis of spread of misinformation on social media based\non three themes- disaster, health, and politics and the review\nfindings. This includes current state of research, theoretical foundations, determinants of misinformation in social\nmedia platforms, and strategies to control the spread of misinformation. Section 5 concludes with the implications and\nlimitations of the paper.\n\n## 1 3\n\n\n### 2 Social media and spread of misinformation\n\nMisinformation arises in uncertain contexts when people are\nconfronted with a scarcity of information they need. During\nunforeseen circumstances, the affected individual or community experiences nervousness or anxiety. Anxiety is one\nof the primary reasons behind the spread of misinformation.\nTo overcome this tension, people tend to gather information\nfrom sources such as mainstream media and official government social media handles to verify the information they\nhave received. When they fail to receive information from\nofficial sources, they collect related information from their\npeercirclesorotherinformalsources,whichwouldhelpthem\nto control social tension [9]. Furthermore, in an emergency\ncontext, misinformation helps community members to reach\na common understanding of the uncertain situation.\n\n#### 2.1 The echo chamber of social media\n\nSocial media has increasingly grown in power and influence\nand has acted as a medium to accelerate sociopolitical movements. Network effects enhance participation in social media\nplatforms which in turn spread information (good or bad) at a\nfaster pace compared to traditional media. Furthermore, due\nto a massive surge in online content consumption primarily\nthrough social media both business organizations and political parties have begun to share content that are ambiguous or\nfake to influence online users and their decisions for financial and political gains [9, 10]. On the other hand, people\noften approach social media with a hedonic mindset, which\nreduces their tendency to verify the information they receive\n\n[9]. Repetitive exposure to contents that coincides with their\npre-existing beliefs, increases believability and shareability\nof content. This process known as the echo-chamber effect\n\n[11] is fueled by confirmation bias. Confirmation bias is the\ntendency of the person to support information that reinforces\npre-existing beliefs and neglect opposing perspectives and\nviewpoints other than their own.\nPlatforms’ structure and algorithms also have an essential role in spreading misinformation. Tiwana et al. [12] have\ndefined platform architecture as ‘a conceptual blueprint that\ndescribes how the ecosystem is partitioned into a relatively\nstable platform and a complementary set of modules that are\nencouraged to vary, and the design rules binding on both’.\nBusiness models of these platforms are based upon maximizing user engagement. For example, in the case of Facebook\nor Twitter, user feed is based on their existing belief or preferences. User feeds provide users with similar content that\nmatches their existing beliefs, thus contributing to the echo\nchamber effect.\nPlatform architecture makes the transmission and retransmission of misinformation easier [12, 13]. For instance,\nWhatsApp has a one-touch forward option that enables users\n\n\n-----\n\nto forward messages simultaneously to multiple users. Earlier, a WhatsApp user could forward a message to 250\ngroups or users at a time, which as a measure for controlling\nthe spread of misinformation was limited to five members\nin 2019. WhatsApp claimed that globally this restriction\nreduced message forwarding by 25% [14]. Apart from platform politics, users also have an essential role in creating\nor distributing misinformation. In a disaster context, people\ntend to share misinformation based on their subjective feeling\n\n[15].\nMisinformation has the power to influence the decisions\nof its audience. It can change a citizen’s approach toward\na topic or a subject. The anti-vaccine movement on Twitter\nduring the 2015 measles (highly communicable disease) outbreak in Disneyland, California, serves as a good example.\nThe movement created conspiracy theories and mistrust on\nthe State, which increased vaccine refusal rate [16]. Misinformation could even influence election of governments by\nmanipulating citizens’ political attitudes as seen in the 2016\nUSA and 2017 French elections [17]. Of late, people rely\nheavily on Twitter and Facebook to collect the latest happenings from mainstream media [18].\nCombating misinformation in social media has been\na challenging task for governments in several countries.\nWhen social media influences elections [17] and health\ncampaigns (like vaccination), governments and international\nagencies demand social media owners to take necessary\nactions to combat misinformation [13, 15]. Platforms began\nto regulate bots that were used to spread misinformation.\nFacebook announced the filtering of their algorithms to combat misinformation, down-ranking the post flagged by their\nfact-checkers which will reduce the popularity of the post or\npage. [17]. However, misinformation has become a complicated issue due to the growth of new users and the emergence\nof new social media platforms. Jang et al. [19] have suggested\ntwoapproachesotherthangovernmentalregulationtocontrol\nmisinformation literary and corrective. The literary approach\nproposes educating users to increase their cognitive ability to\ndifferentiate misinformation from the information. The corrective approach provides more fact-checking facilities for\nusers. Warnings would be provided against potentially fabricated content based on crowdsourcing. Both approaches\nhave limitations; the literary approach attracted criticism as\nit transfers responsibility for the spread of misinformation\nto citizens. The corrective approach will only have a limited\nimpact as the volume of fabricated content escalates [19–21].\nAn overview of the literature on misinformation reveals\nthat most investigations focus on examining the methods\nto combat misinformation. Social media platforms are still\ndiscovering new tools and techniques to mitigate misinformation from their platforms, this calls for a research to\nunderstand their strategies.\n\n\n### 3 Review method\n\nThis research followed a systematic literature review process. The study employed a structured approach based on\nWebster’s Guidelines [22] to identify relevant literature on\nthe spread of misinformation. These guidelines helped in\nmaintaining a quality standard while selecting the literature\nfor review. The initial stage of the study involved exploring\nresearch papers from relevant databases to understand the\nvolumes and availability of research articles. We extended\nthe literature search to interdisciplinary databases too. We\ngathered articles from Web of Science, ACM digital library,\nAIS electronic library, EBSCO host business source premier,\nScienceDirect, Scopus, and Springer link. Apart from this, a\nmanual search was performed in Information Systems (IS)\nscholars’ basket of journals [23] toensurewedidnot miss any\narticles from these journals. We have also preferred articles\nthathaveDataScienceandInformationSystemsbackground.\nThe systematic review process began with keyword search\nusing predefined keywords (Fig. 2). We identified related\nsynonyms such as ’misinformation’, ’rumors’, ’spread’, and\n’social media’ along with their combinations for the search\nprocess. The keyword search was on the title, abstract, and\non the list of keywords. The literature search was conducted\nin the month of April 2020. Later, we revisited the literature\nin December 2021 to include latest publications from 2020\nto 2021.\nIt was observed that scholarly discussion about ‘misinformation and social media’ began to appear in research\nafter 2008. Later in 2010, the topic gained more attention\nwhen Twitter bots were used or spreading fake news on the\nreplacement of a USA Senator [24]. Hate campaigns and fake\nfollower activities were simultaneously growing during that\nperiod. As evident from Fig. 1, showing number of articles\npublished between 2005 and 2021 on misinformation in three\ndatabases: Scopus, Springer, and EBSCO, academic engagement on misinformation seems to have gained more impetus\nafter the 2016 US Presidential election, when social media\nplatforms had apparently influenced the election [20].\nAs Data Science is an interdisciplinary field, the focus of\nour literature review goes beyond disciplinary boundaries.\nIn particular, we focused on the three domains of disaster,\nhealth, and politics. This thematic focus of our review has two\nunderlying reasons (a) the impact of misinformation through\nsocial media is sporadic and has the most damaging effects\nin these three domains and (b) our selection criteria in systematic review finally resulted in research papers that related\nto these three domains. This review has excluded platforms\nthat are designed for professional and business users such\nas LinkedIn and Behance. A rational for the choice of these\nthemes are discussed in the next section.\n\n## 1 3\n\n\n-----\n\n**Fig. 1 Articles published on misinformation during 2005–2021 (Databases; Scopus, Springer, and EBSCO)**\n\n\n#### 3.1 Inclusion–exclusion criteria\n\nFigure 2 depicts the systematic review process followed in\nthis study. In our preliminary search, 2148 records were\nretrieved from databases—all those articles were gathered\nonto a spreadsheet, which was manually cross-checked with\nthe journals linked to the articles. Studies published during\n2005–2021, studies published in English language, articles\npublished from peer-reviewed journals, journals rating and\npapers relevant to misinformation were used as the inclusion\ncriteria. We have excluded reviews, thesis, dissertations, and\neditorials; and articles on misinformation that are not akin to\nsocialmedia.Tofetchthebestfromthesearticles,weselected\narticles that were from top journals, rated above three according to ABS rating and A*, A, and B according to ABDC\nrating. This process, while ensuring the quality of papers,\nalso effectively shortened purview of study to 643 articles of\nacceptable quality. We have not performed track-back and\ntrack-forward on references. During this process, duplicate\nrecords were also identified and removed. Further screening\nof articles based on the title, abstract, and full text (wherever\nnecessary)—brought down the number to 207 articles.\nFurther screening based on the three themes reduced the\nfocus to 89 articles. We conducted a full-text analysis of these\n89 articles. We further excluded articles that had not considered misinformation as a central theme and finally arrived at\n28 articles for detailed review (Table 1).\nThe selected studies used a variety of research methods to\nexamine the misinformation on social media. Experimentation and text mining of tweets emerged as the most frequent\n\n## 1 3\n\n\nresearch methods; there were 11 studies that used experimental methods, and eight used Twitter data analyses. Apart from\nthese, there were three survey methods, two mixed methods,\nand case study methods each, and one opportunistic sampling\nand exploratory study each. The selected literature for review\nincludes nine articles on disaster, eight on healthcare, and\neleven from politics. We preferred papers for review based\non three major social media platforms; Twitter, Facebook,\nand WhatsApp. These are the three social media owners with\nthe highest transmission rates and most active users [25] and\nmost likely platforms for misinformation propagation.\n\n#### 3.2 Coding procedure\n\nInitially both the authors have manually coded the articles\nindividually by reading full text of each article and then\nidentified the three themes; disaster, health, and politics. We\nused an inductive coding approach to derive codes from the\ndata. The intercoder reliability rate between the authors were\n82.1%. Disagreement among authors related to deciding in\nwhich theme few papers fall under were discussed and a resolutionwasarrivedat.LaterweusedNVIVO,aqualitativedata\nanalysis software, to analyze unstructured data to encode and\ncategorize the themes from the articles. The codes emerged\nfrom the articles were categorized into sub-themes and later\nattached to the main themes; disaster, health, and politics.\nNVIVO produced a rank list of codes based on frequency\nof occurrence (“Appendix”). An intercoder reliability check\nwas completed for the data by an external research scholar\nhaving a different areas of expertise to ensure reliability. The\n\n\n-----\n\n**Fig. 2 Systematic literature review process**\n\ncoder agreed upon 26 articles out of 28 (92.8%), which indicated a high level intercoder reliability [49]. The independent\nresearcher’s disagreement about the code for two authors was\ndiscussed between the authors and the research scholar and\na consensus was arrived at.\n\n### 4 Results\n\nWe initially reviewed articles separately from the categories\nof disaster, health, and politics. We first provide emergent\nissues that cut across these themes.\n\n#### 4.1 Social media misinformation research\n\nDisaster, health, and politics emerged as the three domains\n(“Appendix”) where misinformation can cause severe harm,\noften leading to casualties or even irreversible effects. The\n\n\nmitigation of these effects can also demand substantial financial or human resources burden considering the scale of effect\nand risk of spreading negative information to the public\naltogether. All these areas are sensitive in nature. Further,\ndisaster, health, and politics have gained the attention of\nresearchers and governments as the challenges of misinformation confronting these domains are rampant. Besides\nsensitivity, misinformation in these areas has higher potential to exacerbate the existing crisis in society. During the\n2020 Munich security conference, WHO’s Director-General\nnoted: “We are not just fighting an epidemic; we are fighting\nan infodemic”, referring to the faster spread of COVID-19\nmisinformation than the virus [50].\nMore than 6000 people were hospitalized due to COVID19 related misinformation in the first three months of 2020\n\n[51]. As COVID-19 vaccination began, one of the popular\nmyths was that Bill Gates wanted to use vaccines to embed\nmicrochips in people to track them and this created vaccine\n\n## 1 3\n\n\n-----\n\n## 1 3\n\n\n-----\n\nhesitancy among the citizens [52]. These reports show the\nseverity of the spread of misinformation and how misinformation can aggravate a public health crisis.\n\n#### 4.2 Misinformation during disaster\n\nIn the context of emergency situations (unforeseen circumstances), the credibility of social media information has often\nbeen questioned [11]. When a crisis occurs, affected communities often experience a lack of localized information needed\nfor them to make emergency decisions. This accelerates the\nspread of misinformation as people tend to fill this information gap with misinformation or ’improvised news’ [9, 24,\n25]. The broadcasting power of social media and re-sharing\nof misinformation could weaken and slow down rescue operations [24, 25]. As the local people have more access to the\ndisaster area, they become immediate reporters of a crisis\nthrough social media. Mainstream media comes into picture\nonly later. However, recent incidents reveals that voluntary\nreporting of this kind has begun to affect rescue operations\nnegatively as it often acts as a collective rumor mill [9], which\npropagates misinformation. During the 2018 floods in the\nSouth-Indian state of Kerala a fake video on Mullaperiyar\nDam leakage created unnecessary panic among the citizens,\nthus negatively impacting the rescue operations [53]. Information from mainstream media is relatively more reliable\nas they have traditional gatekeepers such as peer reviewers\nand editors who cross-check the information source before\npublication. Chua et al. [28] found that a major chunk of corrective tweets were retweeted from mainstream news media,\nthus mainstream media is considered as a preferred rumor\ncorrection channel, where they attempt to correct misinformation with the right information.\n\n**4.2.1 Characterizing disaster misinformation**\n\nOh et al. [9] studied citizen-driven information processing\nbased on three social crises using rumor theory. The main\ncharacteristic of a crisis is the complexity of information\nprocessing and sharing [9, 24]. A task is considered complex\nwhen characterized by increase in information load, information diversity or rate of information change [54]. Information\noverload and information dearth are the two grave concerns\nthat interrupt the communication between the affected community and a rescue team. Information overload, where too\nmany enquiries and fake news distract a response team, slows\nthem down to recognize valid information [9, 27]. According to Balan and Mathew [55] information overload occurs\nwhenvolumeofinformationsuchascomplexityofwordsand\nmultiple languages that exceeds and cannot be processed by\na human being. Here information dearth in our context is\nthe lack of localized information that is supposed to help the\naffected community to make emergency decisions. When the\n\n\nofficial government communication channels or mainstream\nmedia cannot fulfill citizen’s needs, they resort to information\nfrom their social media peers [9, 27, 29].\nIn a social crisis context, Tamotsu Shibutani [56] defines\nrumoring as collective sharing and exchange of information,\nwhich helps the community members to reach a common\nunderstanding about the crisis situation [30]. This mechanism works in social media, which creates information dearth\nand information overload. Anxiety, information ambiguity\n(source ambiguity and content ambiguity), personal involvement, and social ties are the rumor-causing variables in a\ncrisis context [9, 27]. In general, anxiety is a negative feeling caused by distress or stressful situation, which fabricates\nor produces adverse outcomes [57]. In the context of a crisis or emergency, a community may experience anxiety in\nthe absence of reliable information or in other cases when\nconfronted with overload of information, making it difficult to take appropriate decisions. Under such circumstances,\npeople may tend to rely on rumors as a primary source of\ninformation. The influence level of anxiety is higher during a community crisis than during a business crisis [9].\nHowever, anxiety, as an attribute, varies based on the nature\nof platforms. For example, Oh et al. [9] found that the\nTwitter community do not fall into social pressure as like\nWhatsApp community [30]. Simon et al. [30] developed a\nmodel of rumor retransmission on social media and identified\ninformation ambiguity, anxiety and personal involvement\nas motives for rumormongering. Attractiveness is another\nrumor-causing variable. It occurs when aesthetically appealing visual aids or designs capture a receiver’s attention. Here\nbelievability matters more than the content’s reliability or the\ntruth of the information received.\nThe second stage of the spread of misinformation is misinformation retransmission. Apart from the rumor-causing\nvariables that are reported in Oh et al. [9], Liu et al. [13] found\nsenders credibility and attractiveness as significant variables\nrelated to misinformation retransmission. Personal involvement and content ambiguity can also affect misinformation\ntransmission [13]. Abdullah et al. [25] explored retweeter’s\nmotive on the Twitter platform to spread disaster information.\nContent relevance, early information [27, 31], trustworthiness of the content, emotional influence [30], retweet count,\npro-social behavior (altruistic behavior among the citizens\nduring the crisis), and the need to inform their circle are the\nfactors that drive users’ retweet [25]. Lee et al. [26] have also\nexamined the impact of Twitter features on message diffusion based on the 2013 Boston marathon tragedy. The study\nreported that during crisis events (especially during disasters), a tweet that has less reaction time (time between the\ncrisis and initial tweet) and had higher impact than other\ntweets. This shows that to an extent, misinformation can be\ncontrolled if officials could communicate at the early stage of\na crisis [27]. Liu et al. [13] showed that tweets with hashtags\n\n## 1 3\n\n\n-----\n\ninfluence spread of misinformation. Further, Lee et al. [26]\nfound that tweets with no hashtags had more influence due\nto contextual differences. For instance, usage of hashtags for\nmarketing or advertising has a positive impact, while in the\ncase of disaster or emergency situations, usage of hashtags\n(as in case of Twitter) has a negative impact. Messages with\nno hashtag get widely diffused when compared to messages\nwith the hashtag [26].\nOh et al. [15] explored the behavioral aspects of social\nmedia participants that led to retransmission and spread\nof misinformation. They found that when people believe\na threatening piece of misinformation they received, they\nare more likely to spread it, and they take necessary safety\nmeasures (sometimes even extreme actions). Repetition of\nthe same misinformation from different sources also makes\nit more believable [28]. However, when they realize the\nreceived information was false they were less likely to share\nit with others [13, 26]. The characteristics of the platform\nused to deliver the misinformation also matters. For instance,\nnumbers of likes and shares of the information increases the\nbelievability of the social media post [47].\nIn summary, we found that platform architecture also has\nan essential role in spreading and believability of misinformation. While conducting this systematic literature review,\nweobservedthatmorestudiesondisasterandmisinformation\nare based on the Twitter platform. The six papers out of nine\nthat we reviewed on disaster area were based on the Twitter\nplatform. When a message was delivered in video format,\nit had a higher impact compared to audio or text messages.\nIf the message had a religious or cultural narrative, it led\nto behavioral action (danger control response) [15]. Users\nwere more likely to spread misinformation through WhatsApp than Twitter. It was difficult to find the source of shared\ninformation on WhatsApp [30].\n\n#### 4.3 Misinformation related to healthcare\n\nFrom our review, we found two systematic literature reviews\nthat discusses health-related misinformation on social media.\nYang et al. [58] explores the characteristics, impact and influences of health misinformation on social media. Wang et al.\n\n[59] addresses health misinformation related to vaccines and\ninfectious diseases. This review shows that health-related\nmisinformation, especially on M.M.R. vaccine and autism\nare largely spreading on social media and the government is\nunable to control it.\nThe spread of health misinformation is an emerging issue\nfacing public health authorities. Health misinformation could\ndelay proper treatment to patients, which could further add\nmore casualties to the public health domain [28, 59, 60].\nOften people tend to believe health-related information that\nis shared by their peers. Some of them tend to share their\ntreatment experience or traditional remedies online. This\n\n## 1 3\n\n\ninformation could be in a different context and may not be\neven accurate [33, 34]. Compared to health-related websites,\nthe language used to detail the health information shared\non social media will be simple and may not include essential details [35, 37]. Some studies reported that conspiracy\ntheories and pseudoscience have escalated casualties [33].\nPseudoscience is the term referred to as the false claim, which\npretends as if the shared misinformation has scientific evidence. The anti-vaccination movement on Twitter is one of\nthe examples of pseudoscience [61]. Here the user might have\nshared the information due to the lack of scientific knowledge\n\n[35].\n\n**4.3.1 Characterizing healthcare misinformation**\n\nThe attributes that characterize healthcare misinformation\nare distinctly different from other domains. Chua and Banerjee, [37] identified the characteristics of health misinformation as dread and wish. Dread is the rumor which creates more\npanicandunpleasantconsequences.Forexample,inthewake\nof COVID-19, misinformation was widely shared on social\nmedia, which claimed that children ’died on the spot’ after\nthe mass COVID-19 vaccination program in Senegal, West\nAfrica [61]. This message created panic among the citizens,\nas the misinformation was shared more than 7000 times on\nFacebook [61]. Wish is the type of rumor that gives hope to\nthe receiver (e.g.,: rumor on free medicine distribution) [62].\nDread rumor looks more trustworthy and more likely to get\nviral. Dread rumor was the cause of violence against a minority group in India during COVID-19 [7]. Chua and Banerjee,\n\n[32] added pictorial and textual representations as the characteristics of health misinformation. The rumor that contains\nonly text is textual rumor. Pictorial rumor on the other hand\ncontains both text and images. However, Chua and Banerjee, [32] found that users prefer textual rumor than pictorial.\nUnlike rumors that are circulated during a natural disaster,\nhealth misinformation will be long-lasting, and it can spread\ncutting across boundaries. Personal involvement (the importance of information for both sender and receiver), rumor\ntype and presence of counter rumor are some of the variables that can escalate users’ trusting and sharing behavior\nrelated to rumor [37]. The study of Madraki et al. [46] study\non COVID-19 misinformation /disinformation reported that\nCOVID-19 misinformation on social media differs significantly based on the languages, countries and their culture\nand beliefs. Acceptance of social media platforms as well as\nGovernmental censorship also play an important role here.\nWidespread misinformation could also change collective\nopinion [29]. Online users’ epistemic beliefs could control\ntheir sharing decisions. Chua and Banerjee, [32] argued that\nepistemologically naïve users (users who think knowledge\ncan be acquired easily) are the type of users who accelerate the spread of misinformation on platforms. Those who\n\n\n-----\n\nread or share the misinformation are not likely to follow it\n\n[37]. Gu and Hong [34] examined health misinformation on\nmobile social media context. Mobile internet users are different from large screen users. The mobile phone user might\nhave a more emotional attachment toward the gadget. It also\nmotivates them to believe received misinformation. The corrective effort focused on large screen users may not work\nwith mobile phone users or small screen users. Chua and\nBanerjee [32] suggested that simplified sharing options of\nplatforms also motivate users to share the received misinformation before validating it. Shahi et al. [47] found that\nmisinformation is also propagated or shared even by the verified Twitter handles. They become a part of misinformation\ntransmission either by creating it or endorsing it by liking or\nsharing the information.\nThe focus of existing studies is heavily based on data\nfrom social networking sites such as Facebook and Twitter,\nalthough other platforms too escalate the spread of misinformation. Such a phenomenon was evident in the wake of\nCOVID-19 as an intense trend of misinformation spread was\nreported on WhatsApp, TikTok, and Instagram.\n\n#### 4.4 Social media misinformation and politics\n\nThere have been several studies on the influence of misinformation on politics across the world [43, 44]. Political\nmisinformation has been predominantly used to influence the\nvoters. The USA Presidential election of 2016, French election of 2017 and Indian elections in 2019 have been reported\nas examples where misinformation has influenced election\nprocess [15, 17, 45]. During the 2016 USA election, the partisan effect was a key challenge, where false information was\npresented as if it was from an authorized source [39]. Based\non a user’s prior behavior on the platform, algorithms can\nmanipulate the user’s feed [40]. In a political context, fake\nnews can create more harm as it can influence the voters and\nthe public. Although, fake news has less ‘life’, it’s consequences may not be short living. Verification of fake news\ntakes time and by the time verification results are shared, fake\nnews could achieve its goal [43, 48, 63].\n\n**4.4.1 Characterizing misinformation in politics**\n\nConfirmation bias has a dominant role in social media misinformation related to politics. Readers are more likely to read\nand engage with the information that confirms their preexisting beliefs and political affiliations and reject information\nthat challenges it [46, 48]. For example, in the 2016 USA\nelection, Pro-Trump fake news was accepted by Republicans [19]. Misinformation spreads quickly among people\nwho have similar ideologies [19]. The nature of interface\nalso could escalate the spread of misinformation. Kim and\nDennis [36] investigated the influence of platforms’ infor\n\nmation presentation format and reported that social media\nplatforms indirectly force users to accept certain information;\nthey present information such that little importance is given\nto the source of information. This presentation is manipulative as people tend to believe information from a reputed\nsource and are more likely to reject information that is from\na less-known source [42].\nPennycook et al. [39], and Garrett and Poulsen [40] argued\nthat warning tags (or flagging) on the headline can reduce\nthe spread of misinformation. However, it is not practical to\nassign warning tags to all misinformation as it gets generated\nfaster than valid information. The fact-checking process in\nsocial media also takes time. Hence, people tend to believe\nthat the headlines which do not have warning tags are true and\nthe idea of warning tags will thus not serve any purpose [39].\nFurthermore, it could increase the reader’s belief in warning\ntags and lead to misperception [39]. Readers tend to believe\nthat all information is verified and consider untagged false\ninformation as more accurate. This phenomenon is known as\nthe implied truth effect [39]. In this case, source reputation\nrating will influence the credibility of the information. The\nreader gives less importance to the source that has a low\nrating [17, 50].\n\n### 5 Theoretical perspectives of social media misinformation\n\nWe identified six theories among the articles we reviewed\nin relation to social media misinformation. We found rumor\ntheory was used most frequently among all the studies chosen for our review; the theory was used in four articles as a\ntheoretical foundation [9, 11, 13, 37, 43]. Oh et al. [9], studied citizen-driven information processing on Twitter using\nrumor theory in three social crises. This paper identified\nfour key variables (source ambiguity, personal involvement,\nand anxiety) that spread misinformation. The authors further\nexamined the acceptance of hate rumors and the aftermath\nof community crisis based on the Bangalore mass exodus\nof 2012. Liu et al. [13], examined the reason behind the\nretransmission of messages using rumor theory in disasters.\nHazel Kwon and Raghav Rao [43] investigated how internet\nsurveillance by the government impacts citizens’ involvement with cyber-rumors during a homeland security threat.\nDiffusion theory has also been used in IS research to discern the adoption of technological innovation. Researchers\nhave used diffusion theory to study the retweeting behavior\namong Twitter users (tweet diffusion) during extreme events\n\n[26]. This research investigated information diffusion during\nextreme events based on four major elements of diffusion:\ninnovation, time, communication channels and social systems. Kim et al. [36] examined the effect of rating news\nsources on users’ belief in social media articles based on\n\n## 1 3\n\n\n-----\n\nthree different rating mechanisms expert rating, user article\nrating and user source rating. Reputation theory was used\nto show how users would discern cognitive biases in expert\nratings.\nMurungi et al. [38] used rhetorical theory to argue that\nfact-checkers have less effectiveness on fake news that\nspreads on social media platforms. The study proposed a different approaches by focusing on underlying belief structure\nthat accepts misinformation. The theory was used to identify fake news and socially constructed beliefs in the context\nof Alabama’s senatorial election in 2017. Using third person effect as the theoretical ground, the characteristics of\nrumor corrections on Twitter platform have also been examined in the context of death hoax of Singapore’s first prime\nminister Lee Kuan Yew [28]. This paper explored the motives\nbehind collective rumor and identified the key characteristics\nof collective rumor correction. Using situational crisis communication theory (SCCT), Paek and Hove [44] examined\nhow government could effectively respond to risk-related\nrumors during national-level crises in the context of food\nsafety rumor. Refuting rumor, denying it and attacking the\nsource of rumor are the three rumor response strategies suggested by the authors to counter rumor-mongering (Table 2).\n\n#### 5.1 Determinants of misinformation in social media platforms\n\nFigure 3 depicts the concepts that emerged from our\nreview using a framework of Antecedents-MisinformationOutcomes (AMIO) framework, an approach we adapt from\nSmith HJ et al. [66]. Originally developed to study information privacy, the Antecedent-Privacy-Concerns-Outcomes\n(APCO) framework provided a nomological canvas to\npresent determinants, mediators and outcome variables pertaining to information privacy. Following this canvas, we\ndiscuss the antecedents of misinformation, mediators of misinformation and misinformation outcomes, as they emerged\nfrom prior studies (Fig. 3).\nAnxiety, source ambiguity, trustworthiness, content ambiguity, personal involvement, social ties, confirmation bias,\nattractiveness, illiteracy, ease of sharing options and device\nattachment emerged as the variables determining misinformation in social media.\nAnxiety is the emotional feeling of the person who sends\nor receives the information. If the person is anxious about\nthe information received, he or she is more likely to share or\nspread misinformation [9]. Source ambiguity deals with the\norigin of the message. When the person is convinced of the\nsource of information, it increases his trustworthiness and\nthe person shares it. Content ambiguity addresses the content clarity of the information [9, 13]. Personal involvement\ndenotes how much the information is important for both the\nsender and receiver [9]. Social ties, information shared by\n\n## 1 3\n\n\na family member or social peers will influence the person\nto share the information [9, 13]. From prior literature, it is\nunderstood that confirmation bias is one of the root causes of\npolitical misinformation. Research on attractiveness of the\nreceived information reveals that users tend to believe and\nshare the information that is received on her or his personal\ndevice [34]. After receiving the misinformation from various sources, users accept it based on their existing beliefs,\nand social, cognitive factors and political factors. Oh et al.\n\n[15] observed that during crises, people by default have a\ntendency to believe unverified information especially when\nit helps them to make sense of the situation. Misinformation has significant effects on individuals and society. Loss\nof lives [9, 15, 28, 30], economic loss [9, 44], loss of health\n\n[32, 35] and loss of reputation [38, 43] are the major outcome\nof misinformation emerged from our review.\n\n#### 5.2 Strategies for controlling the spread of misinformation\n\nDiscourse on social media misinformation mitigation has\nresulted in prioritization of strategies such as early communication from the officials and use of scientific evidence [9, 35].\nWhen people realize that the received information or message is false, they are less likely to share that information with\nothers [15]. Other strategies are ’rumor refutation—reducing\ncitizens’ intention to spread misinformation by real information which reduces their uncertainty and serves to control\nmisinformation [44]. Rumor correction models for social\nmedia platforms also employ algorithms and crowdsourcing\n\n[28]. Majority of the papers that we have reviewed suggested\nfact-checking by experts, source rating of the received information, attaching warning tags to the headlines or entire news\n\n[36], and flagging content by the platform owners [40] as the\nstrategies to control the spread of misinformation. Studies\non controlling misinformation in the public health context\nshowed that the government could also seek the help of public health professionals to mitigate misinformation [31].\nHowever, the aforementioned strategies have been criticized for several limitations. Most papers mentioned confirmation bias as having a significant impact on the misinformation mitigation strategies, especially in the political\ncontext where people tend to believe the information that\nmatches their prior belief. Garrett and Poulsen [40] argued\nthat during an emergency situation, misinformation recipient may not be able to characterize the misinformation as\ntrue or false. Thus, providing alternative explanation or the\nreal information to the users have more effect than providing\nfact-checking report. Studies by Garrett and Poulsen [40],\nand Pennycook et al. [39] reveal a drawback of attaching\nwarning tags to news headlines. Once the flagging or tagging of the information is introduced, the information with\nthe absence of tags will be considered as true or reliable\n\n\n-----\n\n**Table 2 Theories used in social media misinformation research**\n\nTheory Description References Theme\n\n\nRumor theory “A collective and collaborative transaction in\nwhich community members offer, evaluate, and\ninterpret information to reach a common\nunderstanding of uncertain situations, to alleviate\nsocial tension, and to solve collective crisis\nproblems” [9]\n\nDiffusion theory In IS research diffusion theory has been used to\ndiscern the adoption of technological innovation.\nDiffusion theory involves “the process by which\nan innovation is communicated through certain\nchannels over time among the members of a\nsocial system.”\n\nReputation theory Reputation is defined as a three-dimensional\nconstruct comprising the types of functional,\nsocial and expressive reputation [36]\n\nRhetorical theory Rhetorical theory is “a way of framing an\nexperience or event—an effort to understand and\naccount for something and the way it functions in\nthe world” [64]\n\nThird person effect Theory of “third-person effect describes an\nindividual’s belief that other people (i.e., the third\nperson), not oneself, are more susceptible to the\nnegative persuasion of the media. The individual\nis consequently motivated to react out of concern\nfor others [28]\n\nSituational crisis communication theory (SCCT) SCCT comprise three elements: “(1) the crisis\nsituation, (2) crisis response strategies, and (3) a\nsystem for matching the crisis situation and crisis\nresponse strategies. The theory states that\neffectiveness of communication strategies is\ndependent on characteristics of the crisis\nsituation.” [65]\n\n**Fig. 3 Determinants of misinformation**\n\n\n\n[9, 13, 15, 37, 43] Disaster, health\n\n[26] Disaster\n\n[36] Politics\n\n[38] Politics\n\n[28] Disaster\n\n[44] Disaster\n\n\n-----\n\ninformation. This creates an implied truth effect. Further, it\nis also not always practical to evaluate all social media posts.\nSimilarly, Kim and Dennis [36] studied fake news flagging\nand found that fake news flags did not influence users’ belief.\nHowever, they created cognitive dissonance and users were\nin search of the truthfulness of the headline. Later in 2017\nFacebook discontinued the fake news flagging service owing\nto its limitations [45]\n\n### 6 Key research gaps and future directions\n\nAlthough, misinformation is a multi-sectoral issue, our systematic review observed that interdisciplinary research on\nsocial media misinformation is relatively scarce. ‘Confirmation bias’ is one of the most significant behavioral problem\nthat motivates the spread of misinformation. However, lack\nof research on it reveals the scope for future interdisciplinary\nresearch across the fields of Data Science, Information Systems and Psychology in domains such as politics and health\ncare. In the disaster context, there is a scope for study on\nthe behavior of a first respondent and an emergency manager to understand their information exchange pattern with\nthe public. Similarly, future researchers could analyze communication patterns between citizens and frontline workers\nin the public health context, which may be useful to design\ncounter-misinformation campaigns and awareness interventions. Since information disorder is a multi-sectoral issue,\nresearchers need to understand misinformation patterns\namong multiple government departments for coordinated\ncounter-misinformation intervention.\nThere is a further dearth of studies on institutional\nresponses to control misinformation. To fill the gap, future\nstudies could concentrate on the analysis of governmental\nand organizational interventions to control misinformation\nat the level of policies, regulatory mechanisms, and communication strategies. For example, in India there is no specific\nlaw against misinformation but there are some provisions\nin the Information Technology Act (IT Act) and Disaster\nManagement Act which can control misinformation and disinformation. An example of awareness intervention is an\ninitiative named ‘Satyameva Jayate’ launched in Kannur district of Kerala, India which focused on sensitizing children at\nschool to spot misinformation [67]. As noted earlier, within\nthe research on Misinformation in the political context, there\nis a lack of research on strategies adopted by the state to\ncounter misinformation. Therefore, building on cases like\n’Satyameva Jayate’ would further contribute to knowledge\nin this area.\nTechnology-based strategies adopted by social media to\ncontrol the spread of misinformation emphasize the corrective algorithms, keywords and hashtags as a solution\n\n[32, 37, 43]. However, these corrective measures have their\n\n## 1 3\n\n\nown limitations. Misinformation corrective algorithms are\nineffective if not used immediately after the misinformation has been created. Related hashtags and keywords are\nused by researchers to find content shared on social media\nplatforms to retrieve data. However, it may not be possible for researchers to cover all the keywords or hashtags\nemployed by users. Further, algorithms may not decipher\ncontent shared in regional languages. Another limitation of\nalgorithms employed by platforms is that they recommend\nandoftendisplaycontentbasedonuseractivitiesandinterests\nwhich limits the users access to information from multiple\nperspectives, thus reinforcing their existing belief [29]. A\nreparative measure is to display corrective information as\n’related stories’ for misinformation. However, Facebook’s\nrelated stories algorithm only activates when an individual\nclicks on an outside link, which limits the number of people\nwho will see the corrective information through the algorithm which turns out to be a challenge. Future research could\ninvestigate the impact of related stories as a corrective measure by analyzing the relation between misinformation and\nfrequency of related stories posted vis a vis real information.\nOur review also found a scarcity of research on the\nspread of misinformation on certain social media platforms\nwhile studies being skewed toward a few others. Of the\nstudies reviewed, 15 articles were concentrated on misinformation spread on Twitter and Facebook. Although, from\nrecent news reports it is evident that largely misinformation\nand disinformation are spread through popular messaging\nplatforms like the ’WhatsApp’, ‘Telegram’, ‘WeChat’, and\n‘Line’, research using data from these platforms are, however, scanty. Especially in the Indian context, the magnitude\nof problems arising from misinformation through WhatsApp\nare overwhelming [68]. To address the lacunae of research\non messaging platforms, we suggest future researchers to\nconcentrate on investigating the patterns of misinformation\nspreading on platforms like WhatsApp. Moreover, message\ndiffusion patterns are unique to each social media platform;\ntherefore, it is useful to study the misinformation diffusion\npatterns on different social media platforms. Future studies\ncould also address the differential roles, patterns and intensity of the spread of misinformation on various messaging\nand photo/ video-sharing social networking services.\nEvident from our review, most research on misinformation is based on Euro-American context and the dominant\nmodels proposed for controlling misinformation may have\nlimited applicability to other regions. Moreover, the popularity of social media platforms and usage patterns are diverse\nacross the globe consequent to cultural differences and political regimes of the region, therefore necessitating researchers\nof social media to take cognizance of empirical experiences\nof ’ left-over’ regions.\n\n\n-----\n\n### 7 Conclusion\n\nTo understand the spread of misinformation on social media\nplatforms, we conducted a systematic literature review in\nthree important domains where misinformation is rampant:\ndisaster, health, and politics. We reviewed 28 articles relevant\nto the themes chosen for the study. This is one of the earliest\nreviews focusing on social media misinformation research,\nespecially based on three sensitive domains. We have discussed how misinformation spreads in the three sectors, the\nmethodologies that have been used by researchers, theoretical perspectives, Antecedents-Misinformation-Outcomes\n(AMIO) framework for understanding key concepts and their\ninter-relationships,andstrategiestocontrolthespreadofmisinformation.\nOur review also identified major gaps in IS research\non misinformation in social media. This includes the need\nfor methodological innovations in addition to experimental\nmethods which have been widely used. This study has some\nlimitations that we acknowledge. We might not have identified all relevant papers on spread of misinformation on social\nmedia from existing literature as some authors might have\nused different keywords and also due to our strict inclusion\nand exclusion criteria. There might also have been relevant\npublications in languages other than English which were\nnot covered in this review. Our focus on three domains also\nrestricted the number of papers we reviewed.\n\n**Author contributions TMS: Conceptualization, Methodology, Investi-**\ngation, Writing—Original Draft, SKM: Writing—Review & Editing,\nSupervision.\n\n**Funding This research did not receive any specific Grant from funding**\nagencies in the public, commercial, or not-for-profit sectors.\n\n#### Declarations\n\n**Conflict of interest On behalf of two authors, the corresponding author**\nstates that there is no conflict of interest in this research paper.\n\n\n### Appendix\n\nCode Sub themes Frequency Themes\n\nSocial crisis situations Situations 43 Disaster\n\nUncertain situations\n\nReal community crisis\nsituations\n\nPost-disaster situation\n\nCrisis situations\n\nAmbiguous situations\n\nUnpredictable crisis situations\n\nUncertain crisis situations\n\nEmergency situations\n\nDisaster situations\n\nEmergency crisis Crisis 36\ncommunication\n\nUnexpected crisis events\n\nCrisis scenario\n\nCrisis management\n\n\nHealth-related conspiracy Conspiracy 33\ntheories\n\nAnti-government rumors Rumor 44 Politics\n\nPolitical headlines Headlines 30\n\nPolitical situations Situations 25\n\nNational threat situations\n\nHomeland threat situations\n\nMilitary conflict situations\n\n## 1 3\n\n\nAddressing health\nmisinformation\ndissemination\n\nGlobal health misinformation\n\nOnline health misinformation\n\nHealth communication\n\nPublic health\n\nHealth pandemic\n\n\nHealth 77 Health\n\n\n-----\n\n### References\n\n1. 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