Detecting and Characterizing Eating-Disorder Communities on Social Media

Abstract

Eating disorders are complex mental disorders and responsible for the highest mortality rate among mental illnesses. Recent studies reveal that user-generated content on social media provides useful information in understanding these disorders. Most previous studies focus on studying communities of people who discuss eating disorders on social media, while few studies have explored community structures and interactions among individuals who suffer from this disease over social media. In this paper, we first develop a snowball sampling method to automatically gather individuals who self-identify as eating disordered in their profile descriptions, as well as their social network connections with one another on Twitter. Then, we verify the effectiveness of our sampling method by: 1. quantifying differences between the sampled eating disordered users and two sets of reference data collected for non-disordered users in social status, behavioral patterns and psychometric properties; 2. building predictive models to classify eating disordered and non-disordered users. Finally, leveraging the data of social connections between eating disordered individuals on Twitter, we present the first homophily study among eating-disorder communities on social media. Our findings shed new light on how an eating-disorder community develops on social media.

Researching Mental Health Disorders in the Era of Social Media: Systematic Review

“…It is employed to classify the polarity of a given text into categories such as positive, negative, and neutral [77]. Several studies [24,28,30,32,34,39, 49,50,52-55,57,60,65-68,70] used the well-known linguistic inquiry and word count (LIWC) [78] to extract potential signals of mental problems from textual content (eg, the word frequency of the first personal pronoun “I” or “me” or of the second personal pronoun, positive and negative emotions being used by a user or in a post). OpinionFinder [79] was used by Bollen et al [71] and SentiStrength [80] was used by Kang et al [27] and by Durahim and Coşkun [47] to carry out sentiment analysis.…”

Analyzing big data in social media: Text and network analyses of an eating disorder forum

“…The authors found that ED communities on Twitter are tightly linked: ED users mostly communicate with and follow other ED users. By comparing the ED sample to two other samples, they showed that ED users have fewer interactions, are active for a shorter time, and express more negative emotions than the comparison samples (Wang et al, ). Other studies have analyzed the usage of weight loss apps by users with underweight BMI goals (Eikey et al, ), tags on Instagram posts to infer mental illness severity (Chancellor, Lin, Goodman, Zerwas, & De Choudhury, ) or modeled networks of Pro‐ED websites (Casilli, Pailler, & Tubaro, ).…”

“…The authors found that ED communities on Twitter are tightly linked: ED users mostly communicate with and follow other ED users. By comparing the ED sample to two other samples, they showed that ED users have fewer interactions, are active for a shorter time, and express more negative emotions than the comparison samples (Wang et al, 2017).…”

Learning Word Embeddings with Chi-Square Weights for Healthcare Tweet Classification

“…To further extract and track healthcare information especially from users' social media profiles, the most basic and crucial task is to discriminate the healthcare-related tweets or target users from the massive pool of tweets and users that are irrelevant to the topic. Wang et al note that prior research on eating disorders only focused on datasets collected from particular forums and communities [4]. Their goal was to identify behavioral patterns and psychometric properties of real users that suffered from eating disorders and not the patterns of people who simply discussed it on Twitter.…”

“…Twitter provides support for accessing tweets via the Twitter API. Healthcare researchers have long been utilizing social media data to conduct their research [3][4][5][6][7]. Because of the popularity of social media platforms such as Twitter, the number of healthcare-related posts is growing fast.…”