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	"paper_id": "2021",
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	"date_generated": "2023-01-19T12:31:18.642806Z"
	},
	"title": "Understanding who uses Reddit: Profiling individuals with a self-reported bipolar disorder diagnosis",
	"authors": [
	{
	"first": "Glorianna",
	"middle": [],
	"last": "Jagfeld",
	"suffix": "",
	"affiliation": {
	"laboratory": "",
	"institution": "Spectrum Centre for Mental Health Research School of Computing and Communications Lancaster University",
	"location": {
	"country": "United Kingdom"
	}
	},
	"email": "g.jagfeld@lancaster.ac.uk"
	},
	{
	"first": "Fiona",
	"middle": [],
	"last": "Lobban",
	"suffix": "",
	"affiliation": {
	"laboratory": "",
	"institution": "Spectrum Centre for Mental Health Research School of Computing and Communications Lancaster University",
	"location": {
	"country": "United Kingdom"
	}
	},
	"email": "f.lobban@lancaster.ac.uk"
	},
	{
	"first": "Paul",
	"middle": [],
	"last": "Rayson",
	"suffix": "",
	"affiliation": {
	"laboratory": "",
	"institution": "Spectrum Centre for Mental Health Research School of Computing and Communications Lancaster University",
	"location": {
	"country": "United Kingdom"
	}
	},
	"email": "p.rayson@lancaster.ac.uk"
	},
	{
	"first": "Steven",
	"middle": [
	"H"
	],
	"last": "Jones",
	"suffix": "",
	"affiliation": {
	"laboratory": "",
	"institution": "Spectrum Centre for Mental Health Research School of Computing and Communications Lancaster University",
	"location": {
	"country": "United Kingdom"
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	},
	"email": "s.jones7@lancaster.ac.uk"
	}
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	"year": "",
	"venue": null,
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	"abstract": "Recently, research on mental health conditions using public online data, including Reddit, has surged in NLP and health research but has not reported user characteristics, which are important to judge generalisability of findings. This paper shows how existing NLP methods can yield information on clinical, demographic, and identity characteristics of almost 20K Reddit users who self-report a bipolar disorder diagnosis. This population consists of slightly more feminine-than masculinegendered mainly young or middle-aged USbased adults who often report additional mental health diagnoses, which is compared with general Reddit statistics and epidemiological studies. Additionally, this paper carefully evaluates all methods and discusses ethical issues.",
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	"paper_id": "2021",
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	"abstract": [
	{
	"text": "Recently, research on mental health conditions using public online data, including Reddit, has surged in NLP and health research but has not reported user characteristics, which are important to judge generalisability of findings. This paper shows how existing NLP methods can yield information on clinical, demographic, and identity characteristics of almost 20K Reddit users who self-report a bipolar disorder diagnosis. This population consists of slightly more feminine-than masculinegendered mainly young or middle-aged USbased adults who often report additional mental health diagnoses, which is compared with general Reddit statistics and epidemiological studies. Additionally, this paper carefully evaluates all methods and discusses ethical issues.",
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	"section": "Abstract",
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	"text": "People who experience extreme mood states that interfere with their functioning, meet the criteria for bipolar disorder (BD) according to the diagnostic manuals Diagnostic and Statistical Manual of Mental Disorders (DSM) (American Psychiatric Association, 2013) and International Classification of Diseases (ICD) (World Health Organisation, 2018). DSM and ICD operationalise extreme mood states in terms of major depressive episodes, 'almost daily depressed mood or diminished interest in activities with additional symptoms for at least 14 days' (World Health Organisation, 2018) and (hypo-)manic episodes, 'a distinct period of abnormally and persistently elevated, expansive, or irritable mood and abnormally and persistently increased goal-directed activity or energy' that lasts at least seven (four) days (American Psychiatric Association, 2013, p. 124) .",
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	"section": "Introduction and related work",
	"sec_num": "1"
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	"text": "DSM and ICD distinguish several BD subtypes based on the lifetime frequency and intensity of (hypo-)manic and depressed episodes. The only requirement for a diagnosis of bipolar I disorder (BD-I) is at least one lifetime manic episode, whereas bipolar II disorder (BD-II) requires at least one hypomanic and one major depressive episode (American Psychiatric Association, 2013, pp. 126, 132) . Cyclothymic disorder applies to numerous periods of hypomanic and depressive symptoms during at least two years that do not meet criteria for hypomanic or major depressive episodes (American Psychiatric Association, 2013, p. 139) .",
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	"section": "Introduction and related work",
	"sec_num": "1"
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	"text": "Bipolar mood episodes are often recurring (Treuer and Tohen, 2010; Gignac et al., 2015) , so many individuals living with BD require life-long treatment (Goodwin et al., 2016) and have a heightened suicide risk (Novick et al., 2010) . However, characteristics and outcomes of people meeting BD criteria are diverse, with some living well, (e.g., Warwick et al., 2019) and even functioning on a high level (Akers et al., 2019) .",
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	"text": "(Treuer and Tohen, 2010;",
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	"text": "Gignac et al., 2015)",
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	"section": "Introduction and related work",
	"sec_num": "1"
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	"text": "Online forums have become an increasingly attractive source for research data, enabling non-reactive data collection, where researchers do not influence data creation, at large scale (Fielding et al., 2016) . Natural language processing (NLP) research in this area has focused on predicting people at risk of BD (Coppersmith et al., 2014; Cohan et al., 2018; Sekuli\u0107 et al., 2018) . Health researchers have explored the lived experience of BD with qualitative analyses of online posts (Mandla et al., 2017; Sahota and Sankar, 2019) . Unlike in clinical studies, usually little or no demographic information is available for online forum users, so it is unclear to what populations these results generalise (Ruths and Pfeffer, 2014) . For example, language differences between Twitter users with self-reported Major depressive disorder (MDD) or Post-traumatic stress disorder (PTSD) correlated highly with their personality and demographic characteristics (Preo\u0163iuc-Pietro et al., 2015) . So it is unclear whether these findings really indicate mental health (MH) diagnoses or other user characteristics.",
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	"section": "Online forums as research data source",
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	"text": "Besides MH-specific platforms (Kramer et al., 2004; Vayreda and Antaki, 2009; Bauer et al., 2013; Latalova et al., 2014; Poole et al., 2015; McDonald and Woodward-Kron, 2016; Campbell and Campbell, 2019) , blogs (Mandla et al., 2017) , and Twitter (Coppersmith et al., 2014; Ji et al., 2015; Saravia et al., 2016; Budenz et al., 2019; Huang et al., 2019) , much recent research of user-generated online content in BD has focused on the international online discussion forum Reddit 1 (Gkotsis et al., 2016 (Gkotsis et al., , 2017 Cohan et al., 2018; Sekuli\u0107 et al., 2018; Sahota and Sankar, 2019; Yoo et al., 2019) .",
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	"section": "The online discussion forum Reddit",
	"sec_num": "1.2"
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	"text": "The platform Reddit is among the most visited internet sites worldwide (Alexa Internet, 2020), hosting a number of subforums ('subreddits') for general topics as well as interest groups. There is a vast and growing amount of BD-related content on Reddit, with more than 50K new posts per month in the four largest BD-related subreddits 2 . Anyone can view posts without registration and the Reddit API offers free access to all historic posts. Reddit profiles do not provide any user characteristics besides the username and sign-up date in a structured format or comparable to a Twitter bio. While some surveys provide general information on Reddit users, none of the BD-specific studies looked at particular user characteristics of their sample, which is important (Amaya et al., 2019) .",
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	"section": "The online discussion forum Reddit",
	"sec_num": "1.2"
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	"text": "The above considerations motivate our research questions: What characteristics of Reddit users who disclose a BD diagnosis can be automatically inferred from their public Reddit information and how do they compare to general Reddit users and clinical populations? What are the ethical considerations around determining users' characteristics and ways to minimise potential negative impacts?",
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	"section": "Research questions and contributions",
	"sec_num": "1.3"
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	"text": "This work has two main contributions, both of which may be relevant to different parts of the CLPsych community. Crucially, the authors are an interdisciplinary team of NLP and clinical psychology researchers, as well as practising clinical psychologists, who regularly consult with people with lived experience of BD in an advisory panel.",
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	"section": "Research questions and contributions",
	"sec_num": "1.3"
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	"text": "First, this paper estimates and discusses clinical, demographic and identity characteristics of Reddit users who self-report a BD diagnosis (see Figure 3 for a visual results summary). This has implications for future BD-focused research on Reddit and helps to contextualise previous work. Moreover, this information is relevant for clinicians who may want to recommend certain online forums to clients and to clinical researchers interested in recruiting via Reddit. Second, this work shows how simple rule-based and off-the-shelf state-of-the-art NLP methods can estimate Reddit user characteristics, and carefully discusses ethical considerations and harm-mitigating ways of doing so. These findings and discussions apply to other, also non-clinical, subgroups of Reddit users. The evaluation with manual annotations evaluates published NLP methods in an applied setting.",
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	"text": "Figure 3",
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	"section": "Research questions and contributions",
	"sec_num": "1.3"
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	"text": "In this work, the identification of Reddit users with lived experience of BD adapts previous approaches based on self-reported diagnosis statements, e.g., 'I was diagnosed with BD today' (Coppersmith et al., 2015; Cohan et al., 2018; Sekuli\u0107 et al., 2018) . Importantly, this captures self-reported diagnoses by a professional and not self-diagnoses, which were excluded. Contrary to existing datasets of Reddit posts by people with a self-reported BD diagnosis, all posts of identified people were retained and not only those unrelated to MH concerns. This enables subsequent research on the lived experience of people with BD. All available Reddit posts (January 05 -March 19) that mentioned 'diagnosis' and a BD term (see below) were downloaded from Google BigQuery. User account meta-data (id, username, UTC timestamp of sign-up) for all matching posts was retrieved via the Reddit python API praw 3 to remove posts by users who had deleted their profile after creation of the BigQuery tables. Each of the 170K posts was classified as self-reported diagnosis post after automatically removing quoted content if it met the following criteria adapted from Cohan et al. (2018) (see Table 1 for examples):",
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	"section": "User identification",
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	"text": "\u2022 Contains at least one condition term for BD.",
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	"section": "User identification",
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	"text": "\u2022 Matches at least one inclusion pattern, i.e., BD diagnosis of any type by a professional.",
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	"section": "User identification",
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	"text": "\u2022 Does not match any exclusion pattern, e.g., self-diagnosis.",
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	"section": "User identification",
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	"text": "145 As someone with a diagnos, my recent CONDITION diagnos, I went to a DOCTOR and got diagnos* CONDITION terms 92 Bipolar, manic depression, BD-I, BD-II, cyclothymia DOCTOR terms 18 Doctor, pdoc, shrink Exclusion patterns 74 Not formally diagnos, self diagnos, she's diagnos* \u2022 The distance between at least one condition term and the beginning or end of an inclusion phrase is less than the experimentally determined threshold of 55 characters.",
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	"section": "Component Number Examples Inclusion patterns",
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	"text": "Subsequently, all posts (id, submissions title, text, subreddit, user id, UTC timestamp of time posted) of the 21K user accounts with at least one self-reported diagnosis post were downloaded via praw. The first author checked the self-reported diagnosis statements of all accounts with more than 1.5K submissions or 200K comments or whose name included 'bot' or 'auto', removing 30 automated user accounts (bots). Finally, 960 user accounts with a self-reported psychotic disorder diagnosis were removed because this constitutes an exclusion criterion for BD (American Psychiatric Association, 2013, pp. 126, 134).",
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	"section": "Component Number Examples Inclusion patterns",
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	"text": "Several NLP methods were applied and compared to extract or infer clinical (MH comorbidities = diagnoses additional to BD), demographic (age, country of residence), and identity (gender) characteristics of Reddit users with a self-reported BD diagnosis. See Appendix A for more details on the age, country, and gender methods and their previously published performance. The first and third author manually annotated self-reported BD diagnoses, age, country, and gender for random included users for evaluation.",
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	"section": "User characteristics extraction/inference",
	"sec_num": "2.2"
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	"text": "Frequencies for other self-reported MH diagnoses were obtained by matching all dataset posts against inclusion patterns for other diagnoses, in the same way as for identifying self-reported BD diagnoses. Condition terms for nine major DSM-5 and ICD-11 diagnoses were extended from Cohan et al. (2018) : Anxiety disorder (Generalised/Social anxiety disorder, Panic disorder), Attention deficit hyperactivity disorder (ADHD), Borderline personality disorder (BPD), MDD, PTSD, Psychotic disorder (Schizophrenia/Schizoaffective disorder), Obsessive compulsive disorder (OCD), Autism spectrum disorder (ASD), and Eating disorder (ED).",
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	"section": "Mental health comorbidities",
	"sec_num": "2.2.1"
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	"text": "Two methods to recognise a user's age relative to one of their posts were compared. An approximate date of birth was calculated from the post timestamp to then calculate the user's age when posting for the first time and their mean age over all posts.",
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	"section": "Age",
	"sec_num": "2.2.2"
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	"text": "\u2022 Self-reported: Reddit users sometimes selfreport their age and gender in a bracketed format, e.g. \u2022 Hybrid: The Hybrid method assigns the extracted age from the Self-reported method if available, and otherwise the predicted age from the Language use method because evaluation revealed that the Self-reported method had higher accuracy but lower coverage than the Language use method (see Section 4.2).",
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	"section": "Age",
	"sec_num": "2.2.2"
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	"text": "The only published method for Reddit user localisation to date (Harrigian, 2018) infers a user's country of residence via a dirichlet process mixture model 4 . It uses the distribution of words, posts per subreddit, and posts per hour of the day (timezone proxy) of a user's up to 250 most recent comments.",
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	"section": "Country of residence",
	"sec_num": "2.2.3"
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	{
	"text": "Three methods to recognise binary gender (feminine (f)/masculine (m)) leveraging different types of information were compared. All three methods pertain to a performative gender view, which posits that people understand their and others' gender identity by certain behaviours (including language) and appearances that society stipulates for bodies of a particular sex (Larson, 2017) . Non-binary gender identities were not included due to a lack of NLP methods to detect them.",
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	"text": "(Larson, 2017)",
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	"section": "Binary gender",
	"sec_num": "2.2.4"
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	"text": "\u2022 Username: The character-based neural network model of Wang and Jurgens (2018) predicts whether a username strongly performs f or m gender, otherwise it assigns no label.",
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	"text": "Wang and Jurgens (2018)",
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	"section": "Binary gender",
	"sec_num": "2.2.4"
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	"text": "\u2022 Self-reported: See Section 2.2.2.",
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	"section": "Binary gender",
	"sec_num": "2.2.4"
	},
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	"text": "\u2022 Language use: The neural network model by Tigunova et al. (2019) predicts gender for Reddit users with at least ten posts from the post texts. It was trained on data automatically labelled with self-reported gender provided by Tigunova et al. (2020) (see Appendix A.1).",
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	"text": "Tigunova et al. (2019)",
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	"section": "Binary gender",
	"sec_num": "2.2.4"
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	"text": "\u2022 Hybrid: Evaluation revealed an accuracy ranking of Username > Self-reported > Language use and the inverse for coverage (Section 4.2). The Hybrid method assigns a binary gender identity in a sequential approach, disregarding possible disagreements between methods: If the Username method found the username to perform f or m gender, it takes this prediction, otherwise assumes the selfreported gender if available, and else resorts to the predictions of the Language use method.",
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	"section": "Binary gender",
	"sec_num": "2.2.4"
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	"text": "At least four main ethical considerations arise for the work presented here: Concerns around (1) consent and (2) anonymity of Reddit users, around the (3) selection, category labels, and assignment of user characteristics (MH diagnoses, age, country, gender), and (4) potentially harmful uses of the presented dataset and methods. The Lancaster University Faculty of Health and Medicine research ethics committee reviewed and approved this study in May 2019 (reference number FHMREC18066).",
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	"section": "Ethical considerations",
	"sec_num": "3"
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	{
	"text": "If and how research on social media data needs to obtain informed consent is debated (Eysenbach and Till, 2001; Beninger et al., 2014; Paul and Dredze, 2017) , mainly because it is not straightforward to determine if posts pertain to a public or private context. Legally, the Reddit privacy policy 5 explicitly allows copying of user contents by third parties via the Reddit API, but it is unclear to what extends users are aware of this (Ahmed et al., 2017) . In practice it is often infeasible to seek retrospective consent from hundreds or thousands of social media users. Current ethical guidelines for social media research (Benton et al., 2017; Williams et al., 2017) and practice in comparable research projects (O'Dea et al., 2015; Ahmed et al., 2017) , regard it as acceptable to waive explicit consent if users' anonymity is protected. Therefore, Reddit users in this work were not asked for consent.",
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	"section": "Consent",
	"sec_num": "3.1"
	},
	{
	"text": "In line with guidelines for ethical social media health research (Benton et al., 2017) , this research only shares anonymised and paraphrased excerpts from posts in publications. Otherwise, it is often possible to recover usernames via a web search with the verbatim post text (see also Section 3.5).",
	"cite_spans": [
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	"text": "As stated in the introduction, user characteristics are important to determine about which populations research on this dataset may generalise. The NLP community increasingly expects data statements for datasets (Bender and Friedman, 2018) , which include speaker age and gender specifications. As Section 4.3 shows, characteristics of Reddit users with a self-reported BD diagnosis deviate from both general Reddit user statistics and epidemiological studies, which therefore do not constitute useful proxies. Relying entirely on self-reported information introduces selection biases because not all user groups may be equally inclined to explicitly share certain characteristics. This motivates using statistical methods to infer Reddit users' age, country, and gender here. The user characteristics comorbid MH issues, age, country, and gender were chosen because they impact peoples' lived experience in BD as discussed in the following. This work identifies users with a self-reported BD diagnosis because collecting posts from BD-specific subreddits does not suffice as carers and people who are unsure if they meet diagnostic criteria also post there. Other self-reported MH diagnoses were extracted because people with BD diagnoses frequently experience additional MH issues (Merikangas et al., 2011) . Self-reported diagnoses capture only users who explicitly and publicly share their diagnosis. This research does not infer any users' MH state. Depp and Jeste (2004) , among others, provide evidence for age-related differences in BD symptoms and experiences, also through increasing importance of physical health comorbidities with ageing. Age estimates were grouped in the same way as in a US survey of Reddit users for comparison.",
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	"start": 212,
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	"section": "Rationales for user characteristics",
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	{
	"text": "Healthcare systems, including provision of MH care, vastly differ between countries, even within Western countries such as the US, UK, and Germany. The MH services people can access may influence their experience of BD, motivating estimation of their country of residence. While Harrigian (2018) predicts longitude/latitude coordinates in 0.5 steps, these are mapped to countries because more fine-grained user localisations are not needed.",
	"cite_spans": [],
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	"sec_num": "3.3"
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	"text": "Using a gender variable in NLP deserves special consideration because it concerns people's identity (Larson, 2017) . Biological sex can impact on the experience of BD, primarily through issues around childbirth and menopause, also related to mood-impacting hormonal changes (Diflorio and Jones, 2010) ; Sajatovic et al. (2011) found effects of gender identity on treatment adherence in BD. This work only uses binary m/f gender labels since no NLP method with more diverse categories was available. The gender recognition methods could cause harm to individual users if they were misgendered and then incorrectly addressed or referred to. This project minimises such harm because the labels only serve to estimate the gender distribution and not to target individual users.",
	"cite_spans": [
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	"start": 100,
	"end": 114,
	"text": "(Larson, 2017)",
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	"text": "This research aims to learn more about Reddit users who share their experiences with BD to yield findings that will ultimately lead to new or improved interventions that support living well with BD. However, most research, even when conducted with the best intentions, suffers from the dual-use problem (Jonas, 1984) , in that it can be misused or have consequences that affect people's life negatively. Adverse consequences of this study could arise for the Reddit users included in the dataset if they are sought out based on their self-reported BD diagnosis to be targeted with, e.g. medication advertisements. The large number of Reddit posts in this dataset can serve as training data for machine learning systems that assign a likelihood to other Reddit/social media users for meeting BD criteria (e.g., Cohan et al., 2018; Sekuli\u0107 et al., 2018) . For example, health insurance companies could misuse this, using applicants' social media profiles in risk assessments.",
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	"sec_num": "3.4"
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	"text": "Based on all above considerations, the dataset will only be shared with other researchers upon request and under a data usage agreement that specifies ethical usage of the dataset as detailed in this section. The dataset release necessarily contains the original post texts but with replaced post and user ids. This requires verbatim web searches with the post texts to seek out individual Reddit users and thus complicates automatisation and scaling. User characteristics, including the manually annotated subsets, will only be shared separately with researchers who justify a specific need for them. To aid transparency, the code and patterns to identify self-reported MH diagnoses, age, and gender are released 6 .",
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	"section": "Transparency: Dataset and code release",
	"sec_num": "3.5"
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	"text": "Users Agreement (%) ",
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	"section": "Variable",
	"sec_num": null
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	"text": "Two authors manually annotated random subsets of users to evaluate all automatically extracted or inferred information according to the annotation guidelines 7 . As shown in Table 2 agreement for all annotations was above 90%, demonstrating feasibility and high reliability.",
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	{
	"start": 174,
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	"sec_num": "4.1"
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	"text": "The annotators checked all extracted selfreported bipolar disorder diagnosis statements of 100 random included users, disagreeing only for three users (see first line of Table 2) 8 . The pattern matching approach for self-reported diagnosis statements mistakenly identified only three users (subsequently removed from the dataset) based on reports of other MH diagnoses where the word bipolar occurred close to the diagnosis term as well 9 .",
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	"text": "To facilitate manual age and gender annotation, 116 users where randomly selected from the 2854 (14%) of users where the Self-reported age or gender extraction method matched. This explains the discrepancy between the coverage of the Self-reported method in Table 3 for the test set and full dataset. The annotators only checked whether date of birthor gender could be unambiguously extracted from all of a users' posts that matched a self-reported age and gender pattern. The test set for the gender evaluation results in Table 3 comprises only users labelled as m/f and excludes one manually identified transgender person. Table 3 shows accuracy and coverage for the user characteristics extraction and inference methods described in Section 2.2 against the manually labelled users for which the annotators could determine a label. For age, the Self-reported method outperforms the Language use method for accuracy but not coverage 10 . The Hybrid method, subsequently used in Section 4.3.2, achieves 99% test set accuracy and 68% coverage on the full dataset. Harrigian's (2018) method assigns a country estimate to every user with 78% test set accuracy. For gender, accuracy decreases from the Username, Self-reported, and Language use method, while coverage increases 11 . The Hybrid gender identification method, used in Section 4.3.2, achieves 97% test set accuracy, gender-labelling 72% of users.",
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	"text": "10 The Language use method for age/gender does not have full coverage because it requires at least ten posts per user. The methods agree for 62.6% of the 1,788 users where both assign an age group.",
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	"text": "11 For 195 users where all three methods assign a gender identity, they agree on 73.8% (90.8% agreement between the Username and Self-reported method, 80% between the Language use and Username or Self-reported method). ",
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	"text": "The following subsections compare characteristics of Reddit users with a self-reported BD diagnosis to general Reddit users and epidemiological statistics. Table 4 shows how many users disclosed other concurrent or lifetime MH diagnoses besides BD. Rates for self-reported MH diagnoses in addition to BD are sightly higher in our dataset compared to the Self-reported MH diagnoses (SMHD) dataset (Cohan et al., 2018) , potentially because our dataset covers 27 more months of posts. Like psychotic disorder (5.2% of users prior to exclusion), a MDD diagnosis is mutually exclusive with BD according to the DSM (American Psychiatric Association, 2013, pp. 126, 134) 12 . A large part of identified self-reported MDD diagnoses were false positives where 'depression' occurred near to a BD diagnosis statement. More conservatively only considering self-reported MDD diagnosis posts that do not also match BD patterns, results in 8.7% users reporting both diagnoses. MDD and Psychotic disorder diagnoses jointly with BD might indicate subsequently changed (mis-)diagnoses or disagreement of professionals. Surveys in Germany (Pfennig et al., 2011) and the US (Hirschfeld et al., 2003) have shown that often more than ten years pass between onset of BD symptoms and receiving the diagnosis, with two thirds of people being misdiagnosed, most frequently with MDD. Moreover, field trials for BD diagnoses with DSM-V criteria only showed moderate clinician agreement (Freedman et al., 2013) . Comorbidity rates for anxiety disorders, BPD and PTSD align with results from epidemiological studies. Rates for comorbid ADHD, OCD, and ED are lower in the Reddit dataset population, which might in part be due to incomplete coverage of the patterns to capture diagnosis self-reports. Additionally, epidemiological studies can be expected to yield higher comorbidity rates because they determine if participants meet criteria for various diagnoses with clinical interviews, whereas Reddit users may not have (or report) diagnoses for every condition they meet the criteria of. Overall, 50.7% of users reported at least one additional MH diagnosis, slightly less than three quarters of surveyed people in the World Mental Health Survey Initiative who met criteria for at least one other DSM-IV disorder besides BD (Merikangas et al., 2011) .",
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	"section": "Reddit users' characteristics",
	"sec_num": "4.3"
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	"text": "More than 2% of users reported an ASD diagnosis in addition to BD, with no epidemiological studies on ASD prevalence with BD yet. Dell'Osso et al. (2019) found significant levels of autistic traits among 43% of people with a BD diagnosis.",
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	"start": 130,
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	"sec_num": "4.3.1"
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	"text": "As shown in Figure 1, 30-49 years old compared to average US Reddit users (Barthel et al., 2016, p. 7) 13 . The age of onset of BD symptoms is most frequently in late adolescence and early adulthood (Pini et al., 2005; Merikangas et al., 2011, p. 6) . In line with this, the majority of Reddit users who disclose a BD diagnosis are between 13-29 years old at their first post. In the Global Burden of Disease study 2013, BD 12months prevalence rates were significantly elated for 20-54 year olds Ferrari et al. (2016, p. 447) .",
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	"text": "In our dataset, almost 80% of the Reddit users are 18-49 years old at their first post.",
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	"text": "As shown in Table 5 , more than 80% of the Reddit users with a self-reported BD diagnosis are estimated to live in the US, and 95% in one of the English-speaking countries US, UK, Canada, Australia. This ranking aligns with site visitors of the Reddit desktop version (Statista.com, 2020), although US users are even more prevalent in the BD dataset. All of the top-5 countries in the dataset have a 12-months prevalence of BD diagnoses above the global average of 0.62% according to the 2017 Global Burden of Disease Study (Global Burden of Disease Collaborative Network, 2018). Figure 2 shows that the Hybrid method assigned feminine gender to slightly more than half of the Reddit users for which it ascribed a gender identity. This sharply contrasts with only 9% feminine vs. 41% masculine gender-performing usernames among Reddit users who posted in the top 10K subreddits with most posts (Wang and Jurgens, 2018) . A survey of adult US Reddit users (Barthel et al., 13 The Barthel et al. (2016) Figure 2 : Binary gender of Reddit users 2016) found that two thirds were men. In epidemiological studies, biological men and women are equally likely to meet criteria for BD overall (Pini et al., 2005 , American Psychiatric Association, 2013 although there is evidence that BD-II is more frequently diagnosed among women (Diflorio and Jones, 2010) . Sajatovic et al. (2011) found that biological men with a BD diagnosis scored significantly lower on masculine gender identity than the general male population, while there were no gender identity differences for biological women. Considering a majority of male Reddit users and sex-equal prevalence of the diagnosis, feminine-gender-identifying people with a BD diagnosis seem to be more likely to use Reddit and/or to disclose their diagnosis. The increased rates of female-gender identifying Reddit users with a selfreported BD diagnosis might also point towards a higher relative frequency of BD-II diagnoses (compared to BD-I) in this population.",
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	"text": "First, unlike in clinical studies with face-to-face interactions, we cannot assume that every Reddit user in the dataset corresponds to one person. Additionally, self-reported diagnoses cannot be confirmed with diagnostic interviews as in clinical research.",
	"cite_spans": [],
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	"section": "Limitations",
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	"text": "Furthermore, there are several limitations to the NLP methods to infer user characteristics. The method to extract self-reported MH diagnoses does not distinguish between actual comorbidities and misdiagnoses or previous diagnoses, for which symptoms may have resolved. Manual evaluation of ten users with BPD comorbidity showed that seven reported concurrent diagnoses, one a BD to BPD change, one a BPD misdiagnosis, and one re-ferred to BD by 'BPD'. Harrigian's (2018) method indicates the predominantly reflected country in a user's most recent posts, disregarding relocations.",
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	"start": 453,
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	"text": "The Self-reported age and gender extraction method is fallible to users providing incorrect information, for example disguising themselves as younger than they really are on dating subreddits. Finally, none of the gender inference methods allow us to estimate how many users identify as transgender or non-binary. Such indications were also too diverse to be captured in the regular expressions for self-reported age and gender. Still, four of the subreddits with more than 10K posts by users with a self-reported BD diagnosis target transgender people, indicating that a proportion of the users in this research may not identify with their born sex.",
	"cite_spans": [],
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	"text": "Most importantly this work provides the first large-scale characterisation of Reddit users with a self-reported BD diagnosis, who are on average 27.7 years old at their first post, seem to overwhelmingly live in the US, and are more likely to identify with the feminine gender. Insofar they deviate from general Reddit as well as epidemiological statistics and also from participants in clinical studies.",
	"cite_spans": [],
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	"section": "Health research implications",
	"sec_num": "5.2"
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	"text": "A large meta-analysis of psychological interventions for BD (Oud et al., 2016) showed that in 55 trials conducted across twelve countries (35% in the US) comprising 6,060 adults with BD, 89% had recruited participants with a mean age higher than the 30 year-average of adult Reddit users with a self-reported BD diagnosis. 67% of the trials recruited a higher percentage of females than the 52% figure in the Reddit dataset (Oud et al., 2016, Table DS2 ). This cautions against generalising findings from Reddit data to all people with a BD diagnosis, but stresses its complementary role to clinical studies with different selection biases.",
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	"start": 60,
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	"text": "Another important implication is that NLP analysis of Reddit social media users largely confirmed high prevalence rates for comorbid MH conditions with BD from epidemiological studies. Besides clinically established comorbidities with, e.g., Anxiety disorder and ADHD, the present analysis also revealed substantial prevalence of ASD, for which there is little clinical research to date. Reddit may constitute a useful platform to learn about the experiences of people with BD with such currently under-researched comorbidities and may be a way to target them for recruitment to clinical studies.",
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	"text": "This work evaluated state-of-the-art methods to infer Reddit user characteristics (Harrigian, 2018; Wang and Jurgens, 2018; Tigunova et al., 2019) and demonstraed their utility in applied research. A hybrid method achieved the best accuracy and coverage for age and gender identity by using high-accuracy information from self-reports (or a gender-performing username) when available, filling in information for more users with less accurate predictions from a neural network language usebased method (Tigunova et al., 2019) .",
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	"text": "Importantly, gender-inference methods so far are limited to detecting binary gender, although, e.g., 0.4% of the US population identify as transgender (Meerwijk and Sevelius, 2017) . Off-the-shelf NLP tools supporting a wider range of gender identities may be more inclusive and give more visibility to these groups of people in research. However, important ethical considerations arise around identifying people with transgender and non-binary gender identities, which are often stigmatised.",
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	"text": "(Meerwijk and Sevelius, 2017)",
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	"section": "NLP research implications",
	"sec_num": "5.3"
	},
	{
	"text": "This paper set out to automatically profile Reddit users under consideration of ethical aspects. A combination of pattern-based and previously published NLP methods served to estimate clinical, demographic, and identity characteristics of nearly 20K Reddit users with a self-reported BD diagnosis. Half of the Reddit users disclosed MH diagnoses besides BD and 80% were located in the US. From the users for which age or gender could be estimated, 80% were between 18-49 years old and 52% performed or identified with feminine gender.",
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	"text": "These findings indicate about which populations BD-focused research on Reddit may generalise. Additionally, this work may serve as a model for how to provide more information on other specific Reddit populations as requested by recent transparency and accountability movements in NLP. (Tigunova et al., 2020) with the HAM open-source implementation 15 with the hyper-parameters specified by Tigunova et al. (2020) (128 CNN filters of size 2, attention layer with 150 units, 70 training epochs). Likely due to random seed variation, our trained age model had an area under the curve (AUROC) score of 0.80 compared to 0.88 in Tigunova et al. (2020) . Our trained gender model had 84.9% accuracy on the RedDust test set compared to 86.0% reported by Tigunova et al. (2020) .",
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	"text": "Two corrections were applied prior to the Hybrid method: The first author checked all users with a self-reported average posting age below 16 or above 60. Age at account creation predictions younger than 13 by the Language use approach were discarded as Reddit requires an age of at least 13 when signing up.",
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	"section": "A.2 Age: Hybrid method",
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	{
	"text": "The Reddit country inference method (Harrigian, 2018) initially was a proprietary project but later the first author, Keith Harrigian, rebuilt it for the public release 16 used in this work. Therefore, the training data and model performance, provided by Keith Harrigian in personal email communication on 5th March 2021, slightly differ from the original publication. The training data consists of 56,853 automatically location-labelled users (top 5: 68.8% US, 9.4% Canada, 7.0% UK, 3.3% Australia, 1.0% Germany), of which 8.2% were identified based on self-reported locations in r/AmateurRoomPorn and the remainder by selfreported locations in reply to 'Where are you 14 younger than 14, 14-23, 24-45, 46-65, 66+, relative to the user's most recent post 15 https://github.com/Anna146/ HiddenAttributeModels 16 https://github.com/kharrigian/smgeo from?' questions (Harrigian, 2018) . Label precision was 97.6% in a manual evaluation of 500 users 17 . The 'Global' (as opposed to US only) model was used to predict user locations, which achieves 35.6% accuracy at 100 miles in 5-fold cross validation, equal to the originally reported performance in Harrigian (2018) . Overall country-level accuracy is 81.9% and is generally higher for users with more training data (95.1% US, 65.1% Canada, 82.8% UK, 44.1% Australia, 41.1% Germany).",
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	"section": "A.3 Country",
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	{
	"text": "A.4 Gender: Username method Wang and Jurgens (2018, p. 38) trained their long short-term memory (LSTM) gender inference model on 80% of 4,900,250 Twitter and 367,495 Reddit usernames, automatically labelled with selfreported m or f gender identity. Following them, the present work assumes usernames to perform masculine (m) gender for model predictions of 0.1 or lower, and feminine (f) for 0.9 or higher. This model and setting achieved 0.92 precision with 0.18 recall in 10% held-out Twitter and Reddit username test data (Wang and Jurgens, 2018, Figure 5 in supplementary material).",
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	"section": "A.3 Country",
	"sec_num": null
	},
	{
	"text": "https://www.reddit.com/ 2 r/bipolar, r/BipolarReddit, r/bipolar2, r/bipolarSOs",
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	"text": "https://github.com/praw-dev/praw",
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	"sec_num": null
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	"text": "https://github.com/kharrigian/smgeo",
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	"section": "",
	"sec_num": null
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	{
	"text": "https://www.redditinc.com/policies/ privacy-policy",
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	"section": "",
	"sec_num": null
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	{
	"text": "https://github.com/glorisonne/reddit_ bd_user_characteristics",
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	"section": "",
	"sec_num": null
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	{
	"text": "https://github.com/glorisonne/reddit_ bd_user_characteristics/blob/master/ ManualAnnotationGuidelines.pdf8 No attempt was made to evaluate recall of user identification. Given an international prevalence of meeting BD criteria of about 2%(Merikangas et al., 2011) and expecting numbers of posts per account close to the average of 1,224 in the collected dataset, it was deemed infeasible to manually check all posts of randomly selected user accounts for self-reported bipolar disorder diagnosis statements.9 Paraphrased excerpts of incorrectly identified selfreported BD diagnoses: 'clinical depression with bipolar tendencies', 'diagnosed with BPD today, thought it was BD for years', 'diagnosed with depression, but sure I've got bipolar'.",
	"cite_spans": [],
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	"sec_num": null
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	{
	"text": "The dataset includes users with self-reported MDD but not psychotic disorder because depression but not psychosis is a core aspect of extreme mood, our focus of future research.",
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	"text": "https://github.com/kharrigian/smgeo# dataset-noise",
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	"sec_num": null
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	],
	"back_matter": [
	{
	"text": "We would like to thank Anna Tigunova and Keith Harrigian for their assistance in applying their Reddit user profiling NLP tools. We would also like to express our heartfelt thanks to Daisy Harvey, Stephen Mander, and the anonymous reviewers for helpful comments on a draft version of this article, to Andrew Moore for testing the code release, and to Alistair Baron for the initial idea for this work.",
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	"section": "Acknowledgements",
	"sec_num": null
	}
	],
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	"FIGREF0": {
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	"type_str": "figure",
	"num": null,
	"text": "Age of Reddit users"
	},
	"TABREF0": {
	"html": null,
	"type_str": "table",
	"text": "Components of patterns to identify English self-reported diagnosis statements; *: wildcard",
	"content": "<table/>",
	"num": null
	},
	"TABREF3": {
	"html": null,
	"type_str": "table",
	"text": "",
	"content": "<table><tr><td>: Number of users in manual annotation, raw an-</td></tr><tr><td>notator agreement, and label distributions after resolv-</td></tr><tr><td>ing disagreements in discussion (?: no label assigned</td></tr><tr><td>due to lack of user-provided information on Reddit)</td></tr></table>",
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	"TABREF4": {
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	"content": "<table><tr><td>: Accuracy ( correct total ) for user metadata extraction and inference methods (see Section 2.2) for manually annotated users (test), coverage ( predicted total ) for manually annotated (test) and all (all, n=19,685) users</td></tr><tr><td>4 Results and discussion</td></tr><tr><td>The self-reported BD diagnosis matching</td></tr><tr><td>method identified 19,685 Reddit users who</td></tr><tr><td>together had 21,407,595 public Reddit posts</td></tr><tr><td>between March 2006 and March 2019. Compared</td></tr><tr><td>to 9K unique user accounts who posted in the four</td></tr><tr><td>largest BD-related subreddits in May 2020, this</td></tr><tr><td>likely only constitutes a small fraction of Reddit</td></tr><tr><td>users with a BD diagnosis that could be reliably</td></tr><tr><td>automatically identified (see following subsection).</td></tr></table>",
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	"TABREF6": {
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	"text": "Self-reported comorbid diagnoses with BD in this work, the SMHD dataset, and epidemiological studies:",
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	"TABREF8": {
	"html": null,
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	"content": "<table><tr><td>: Top 5 estimated countries of residence of Red-</td></tr><tr><td>dit users with a self-reported BD diagnosis, location</td></tr><tr><td>of reddit.com site visitors (Statista.com, 2020) and 12-</td></tr><tr><td>months prevalence of BD (Global Burden of Disease</td></tr><tr><td>Collaborative Network, 2018)</td></tr></table>",
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	},
	"TABREF10": {
	"html": null,
	"type_str": "table",
	"text": "Mark Zimmerman and Theresa A. Morgan. 2013. Problematic Boundaries in the Diagnosis of Bipolar Disorder: The Interface with Borderline Personality Disorder. Current Psychiatry Reports, 15(422). Age and gender: Language use Tigunova et al.'s (2019) HAM CNN-attn model predicts an age group 14 and gender for Reddit users with at least ten posts based on their up to 100 most recent posts. Separate HAM CNN-attn models were trained on the RedDust dataset",
	"content": "<table><tr><td>A Further method details</td></tr><tr><td>A.1</td></tr></table>",
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