Social interactions in online eating disorder communities: A network perspective

Review of machine learning solutions for eating disorders

BACKGROUND

Eating Disorders (EDs) are one of the most complex psychiatric disorders, with significant impairment of psychological and physical health, and psychosocial functioning, and are associated with low rates of early detection, low recovery and high relapse rates. This underscores the need for better diagnostic and treatment methods.

OBJECTIVE

This narrative review explores current Machine Learning (ML) and Artificial Intelligence (AI) applications in the domain of EDs, with a specific emphasis on clinical management in treatment settings. The primary objective are to (i) decrease the knowledge gap between ED researchers and AI-practitioners, by presenting the current state-of-the-art AI applications (including models for causality) in different ED use-cases; (ii) identify limitations of these existing AI interventions and how to address them.

RESULTS

AI/ML methods have been applied in different ED use-cases, including ED risk factor identification and incidence prediction (including the analysis of social media content in the general population), diagnosis, monitoring patients and treatment response and prognosis in clinical populations. A comparative analysis of AI-techniques deployed in these use-cases have been performed, considering factors such as complexity, flexibility, functionality, explainability and adaptability to healthcare constraints.

CONCLUSION

Multiple restrictions have been identified in the existing methods in ML and Causality in terms of achieving actionable healthcare for ED, like lack of good quality and quantity of data for models to train on, while requiring models to be flexible, high-performing, yet being explainable and producing counterfactual explanations, for ensuring the fairness and trustworthiness of its decisions. We conclude that to overcome these limitations and for future AI research and application in clinical management of ED, (i) careful considerations are required with regards to AI-model selection, and (ii) joint efforts from ED researcher and patient community are essential in building better quality and quantity of dedicated ED datasets and secure AI-solution framework.

Pro-anorexia coaches prey on individuals with eating disorders

OBJECTIVE

While studies have focused on pro-ana communities and pro-anorexia websites, no research has been conducted on the presence of pro-anorexia coaches within these communities. This study aimed to gain insight into the modus operandi of pro-anorexia coaches.

METHOD

First, three fake profiles were used to attempt interaction with pro-anorexia coaches (n = 31). Second, an online questionnaire on experiences with pro-anorexia coaches was completed by 79 respondents. Third, a follow-up in-depth interview was conducted with 14 of these respondents.

RESULTS

The results show that pro-anorexia coaches' behavior fits a five-staged model which has similarities to stages of online grooming. They focus on (1) building trust and developing a dependency relationship with the aim of (2) obtaining sexually explicit materials. Subsequently, they (3) increase pressure, (4) utilize blackmail methods with acquired content, and (5) groom respondents to meet in person.

DISCUSSION

Pro-anorexia coaches deliberately abuse vulnerable young people who suffer from eating disorders to receive sexually explicit material or to meet face-to-face with a sexual intention. This study shows that the modus operandi of pro-ana coaches is similar to online grooming. Clinical professionals need to be aware of this practice to detect victims and potential victims in order to support and treat them. Prevention is invaluable to further combat pro-anorexia coaches and protect potential victims.

PUBLIC SIGNIFICANCE

Individuals with an eating disorder are avid internet users. A minority frequent pro-anorexia websites and fora seeking help in losing weight. This study showed that pro-anorexia coaches prey on these individuals, often to obtain sexually explicit content. The modus operandi of pro-anorexia coaches shows similarities to online grooming. Awareness of this phenomenon and the way in which pro-anorexia coaches operate is valuable for clinicians, parents, and teachers.

Motives for using social networking sites: a uses & gratifications perspective amongst people with eating disorder symptoms

Studies investigating motives for social networking sites (SNS) use amongst people with eating disorder (ED) symptoms are scarce. The uses and gratifications theory states that people actively select media content to gratify their individual needs and this may help to explain why individuals with eating disorder symptoms choose to expose themselves to specific media content and how they choose to respond. Some evidence exists that readiness to change may be a factor related to SNS use patterns in this population. The aim of the present study was to investigate the potential relationship between readiness to change ED cognitions/behaviors and SNS use motives in individuals with current or past ED symptoms, employing a uses and gratifications theory perspective. Participants were recruited from ED recovery websites; 103 individuals (16-55 years old) provided complete data. As hypothesized, ED symptom severity was inversely associated with readiness to change and time spent on (specific) SNS was not associated with ED symptom severity. The results of hierarchical regression analyses further suggested that if individuals felt less capable of overcoming their dysfunctional cognitions/behaviors about their body/eating (i.e., were less ready to change), they were more likely to be motivated to use SNSs for the purpose of impressing others and reputation management. The clinical implication is that high amounts of SNS use may not necessarily be harmful for people with EDs or recovering from EDs, whereas using SNSs for the purposes of impression management could potentially be related to being less ready to recover.

Applying Stages of Change Model to Examine Online Peer Communication on Binge Eating

Despite the distress and disruption associated with eating disorder (ED), people struggling with EDs are often ambivalent about their eating issues and unmotivated for recovery. Rather than seek professional help, these people tend to turn to online ED groups for information and support. Using the stages of change model, this study investigates the characteristics of online peer communication around binging vis-à-vis participants' motivation and readiness for behavioral change. Our results illustrate how individuals with binging issues in different motivational stages discuss their problematic eating online and provides insights into their ambivalence toward treatment and relapses into binging. This study further clarifies how people with binging issues feel trapped in a cycle of dieting-binging, which is observed to be undergirded by unresolved weight-related issues, and how they cope with the stressful relationship between eating and body weight through their postings on social media. These findings suggest that healthcare providers should promote healthier methods of addressing the weight gain from binge eating and provide support tailored to individuals' motivational stage in breaking the dieting-binging cycle.

Advantages and Disadvantages of Internet Use: The Perspective of Women with Eating Disorders Experience

Even though the internet is a common source of information and treatment for people with eating disorder (ED) experience, the motives for illness-related searches have rarely been investigated beyond the perceived negatives. This study explores how women with ED experience reflect upon the advantages and disadvantages of their ED-related internet use. We expand the framework of the Uses and Gratifications Theory (U&G) into the context of users with ED experience through 30 semi-structured interviews with women with ED experience, aged 16 to 28, who live in the Czech Republic. Thematic analysis revealed four themes related to the pros and cons of their internet usage: ED-related Information Content; Internet Features Important to Users; Body Image; and Social Interaction. The results challenge the binary view of ED-related internet use and question some presumptions of U&G Theory within the specific context of users with ED experience.

Identifying and Characterizing Medical Advice-Seekers on a Social Media Forum for Buprenorphine Use

Background: Online communities such as Reddit can provide social support for those recovering from opioid use disorder. However, it is unclear whether and how advice-seekers differ from other users. Our research addresses this gap by identifying key characteristics of r/suboxone users that predict advice-seeking behavior. Objective: The objective of this analysis is to identify and describe advice-seekers on Reddit for buprenorphine-naloxone use using text annotation, social network analysis, and statistical modeling techniques. Methods: We collected 5258 posts and their comments from Reddit between 2014 and 2019. Among 202 posts which met our inclusion criteria, we annotated each post to determine which were advice-seeking (n = 137) or not advice-seeking (n = 65). We also annotated each posting user’s buprenorphine-naloxone use status (current versus formerly taking and, if currently taking, whether inducting or tapering versus other stages) and quantified their connectedness using social network analysis. To analyze the relationship between Reddit users’ advice-seeking and their social connectivity and medication use status, we constructed four models which varied in their inclusion of explanatory variables for social connectedness and buprenorphine use status. Results: The stepwise model containing “total degree” (p = 0.002), “using: inducting/tapering” (p < 0.001), and “using: other” (p = 0.01) outperformed all other models. Reddit users with fewer connections and who are currently using buprenorphine-naloxone are more likely to seek advice than those who are well-connected and no longer using the medication, respectively. Importantly, advice-seeking behavior is most accurately predicted using a combination of network characteristics and medication use status, rather than either factor alone. Conclusions: Our findings provide insights for the clinical care of people recovering from opioid use disorder and the nature of online medical advice-seeking overall. Clinicians should be especially attentive (e.g., through frequent follow-up) to patients who are inducting or tapering buprenorphine-naloxone or signal limited social support.

What Topics Do Members of the Eating Disorder Online Community Discuss and Empathize with? An Application of Big Data Analytics

Given the stigma of eating disorders (EDs), anonymous online communities of individuals with EDs can play a critical role in their treatment. In our study, we aimed to identify prevalent topics related to EDs in one community. To discover latent topics in an online community dedicated to EDs, we applied an automated text-mining approach to topic modeling after collecting data from Reddit created between January 2011 and September 2020. As a result of topic modeling, topics regarding emotional support, informational support, and inquiries about EDs were discovered. In frequently asked questions and advice about EDs in the online community, community members empathized more with topics soliciting emotional support rather than informational support. Despite the importance of online communities, studies on this topic, especially those analyzing online conversations, have remained limited. By demonstrating the role of online communities in supplementary interventions, our findings can support clinicians in caring for patients with EDs.

#EatingDisorderRecovery: a qualitative content analysis of eating disorder recovery-related posts on Instagram

PURPOSE

Limited research has evaluated the role of Social Networking Sites (SNS) in eating disorder (ED) recovery. While research has demonstrated the deleterious effects of pro-ED SNS content, less is known regarding SNS content documenting ED recovery. This study evaluates orientation towards help-seeking, ongoing ED warning signs and recovery themes on ED recovery SNS hashtags.

METHODS

600 Instagram posts were retrieved from two popular hashtags: #EDrecovery and #EatingDisorderRecovery. They were categorized into four thematic concerns: Food, Quotes, People or Others. Posts were analysed for behavioural and psychological signs of ED based on the Mental Health First Aid Eating Disorders Guidelines, and whether they encouraged seeking professional help. Thematic qualitative analysis to evaluate themes posted on recovery hashtags was conducted.

RESULTS

Of the 600 posts, 405 were used for analysis. The majority of posts were on Food (49.6%), Quotes (24.2%) and People (22.7%). Behavioural and psychological signs suggestive of EDs were present in 18.0% and 22.5% of images, respectively. Only 9.4% of posts encouraged seeking professional help. Important themes that emerged from the qualitative analysis included the recovery journey, increased awareness and stigma for EDs and the development of a supportive community.

CONCLUSIONS

Despite identifying with ED recovery, posts had a high prevalence of ongoing ED behaviour and low prevalence of help-seeking. Thematic analysis emphasized the role of recovery as a journey and the role of stigma and community in recovery. These findings suggest that SNSs could potentially be leveraged as a platform to improve help-seeking and encourage recovery for users with eating disorders.

LEVEL OF EVIDENCE

Level V, descriptive study.

Universal programs to prevent eating disorders in children and adolescents: A scoping review of ethical, legal, organizational and social impacts

BACKGROUND

Appropriate and timely consideration of ethical, legal, organizational, and social issues in universal preventive programs for eating disorders (UPPED) are relevant for the approval, funding and implementation of health-policy decision making.

OBJECTIVE

To identify and analyse the ethical, legal, organizational, and social aspects involved in interventions aimed at the universal prevention of eating disorders (ED) in children, pre-adolescents and adolescents in the school settings.

METHOD

A scoping review of the literature was carried out. MEDLINE, EMBASE, CENTRAL, PsycINFO, and Social Science Citation Index were searched for studies published in English or Spanish. The quality of the studies was assessed using specific scales for each study design.

RESULTS

Fourteen studies were included: one scoping review; four narrative reviews, six observational studies, two qualitative studies, and one mixed methods study. Results were narratively synthesised according to: (1) equity; (2) gender perspective; (3) potential harm; (4) participants and facilitators profile; (5) feasibility; and (6) acceptability.

CONCLUSIONS

Interactive programs with relevant contents for participants have greater acceptability. Programs focussed on developing competencies can reduce the risk of potential harm. Incorporating a gender perspective contributes to improving equity. Teachers with prior training in ED are well suited as facilitators of these programs.

Machine learning of language use on Twitter reveals weak and non-specific predictions

Depressed individuals use language differently than healthy controls and it has been proposed that social media posts can be used to identify depression. Much of the evidence behind this claim relies on indirect measures of mental health and few studies have tested if these language features are specific to depression versus other aspects of mental health. We analysed the Tweets of 1006 participants who completed questionnaires assessing symptoms of depression and 8 other mental health conditions. Daily Tweets were subjected to textual analysis and the resulting linguistic features were used to train an Elastic Net model on depression severity, using nested cross-validation. We then tested performance in a held-out test set (30%), comparing predictions of depression versus 8 other aspects of mental health. The depression trained model had modest out-of-sample predictive performance, explaining 2.5% of variance in depression symptoms (R² = 0.025, r = 0.16). The performance of this model was as-good or superior when used to identify other aspects of mental health: schizotypy, social anxiety, eating disorders, generalised anxiety, above chance for obsessive-compulsive disorder, apathy, but not significant for alcohol abuse or impulsivity. Machine learning analysis of social media data, when trained on well-validated clinical instruments, could not make meaningful individualised predictions regarding users’ mental health. Furthermore, language use associated with depression was non-specific, having similar performance in predicting other mental health problems.

Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)-Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study

BACKGROUND

Eating disorders affect an increasing number of people. Social networks provide information that can help.

OBJECTIVE

We aimed to find machine learning models capable of efficiently categorizing tweets about eating disorders domain.

METHODS

We collected tweets related to eating disorders, for 3 consecutive months. After preprocessing, a subset of 2000 tweets was labeled: (1) messages written by people suffering from eating disorders or not, (2) messages promoting suffering from eating disorders or not, (3) informative messages or not, and (4) scientific or nonscientific messages. Traditional machine learning and deep learning models were used to classify tweets. We evaluated accuracy, F1 score, and computational time for each model.

RESULTS

A total of 1,058,957 tweets related to eating disorders were collected. were obtained in the 4 categorizations, with The bidirectional encoder representations from transformer-based models had the best score among the machine learning and deep learning techniques applied to the 4 categorization tasks (F1 scores 71.1%-86.4%).

CONCLUSIONS

Bidirectional encoder representations from transformer-based models have better performance, although their computational cost is significantly higher than those of traditional techniques, in classifying eating disorder-related tweets.

Evaluation of the Effects of Telepsychotherapy in the Treatment and Prevention of Eating Disorders in Adolescents

According to the WHO definition, "telemedicine is the provision of health services, where distance is a critical factor, by all health professionals who use information and communication technologies for the exchange of valid information for the diagnosis, treatment and prevention of diseases, research and evaluation, and for the continuous training of health professionals, all in the interest of advancing the health of individuals and their communities". The purpose of our review work is specifically to investigate the effects of telemedicine in the treatment and prevention of eating disorders in adolescents. From June 2021 to (September 2021) in the databases of the Web of Science, EMBASE, PsycINFO and CINHAL, using search terms such as telehealth, eating disorder, adolescents, Internet/online treatments CBT and FB-T, anorexia nervosa, bulimia nervosa and binge eating disorder. The articles resulting from the search phases in the databases listed above produced a total of 176 items. Once the procedures for selecting the works were completed, only four studies were included in the review. Modern e-health psychological approaches in the treatment of eating disorders provide potential bases of continuous assistance that are decidedly less burdensome in the costs of territorial services in the case that they are not identified as necessary.

Developing a standardized protocol for computational sentiment analysis research using health-related social media data

OBJECTIVE

Sentiment analysis is a popular tool for analyzing health-related social media content. However, existing studies exhibit numerous methodological issues and inconsistencies with respect to research design and results reporting, which could lead to biased data, imprecise or incorrect conclusions, or incomparable results across studies. This article reports a systematic analysis of the literature with respect to such issues. The objective was to develop a standardized protocol for improving the research validity and comparability of results in future relevant studies.

MATERIALS AND METHODS

We developed the Protocol of Analysis of senTiment in Health (PATH) based on a systematic review that analyzed common research design choices and how such choices were made, or reported, among eligible studies published 2010-2019.

RESULTS

Of 409 articles screened, 89 met the inclusion criteria. A total of 16 distinctive research design choices were identified, 9 of which have significant methodological or reporting inconsistencies among the articles reviewed, ranging from how relevance of study data was determined to how the sentiment analysis tool selected was validated. Based on this result, we developed the PATH protocol that encompasses all these distinctive design choices and highlights the ones for which careful consideration and detailed reporting are particularly warranted.

CONCLUSIONS

A substantial degree of methodological and reporting inconsistencies exist in the extant literature that applied sentiment analysis to analyzing health-related social media data. The PATH protocol developed through this research may contribute to mitigating such issues in future relevant studies.

Development of an Offline-Friend Addiction Questionnaire (O-FAQ): Are most people really social addicts?

A growing number of self-report measures aim to define interactions with social media in a pathological behavior framework, often using terminology focused on identifying those who are 'addicted' to engaging with others online. Specifically, measures of 'social media addiction' focus on motivations for online social information seeking, which could relate to motivations for offline social information seeking. However, it could be the case that these same measures could reveal a pattern of friend addiction in general. This study develops the Offline-Friend Addiction Questionnaire (O-FAQ) by re-wording items from highly cited pathological social media use scales to reflect "spending time with friends". Our methodology for validation follows the current literature precedent in the development of social media 'addiction' scales. The O-FAQ had a three-factor solution in an exploratory sample of N = 807 and these factors were stable in a 4-week retest (r = .72 to .86) and was validated against personality traits, and risk-taking behavior, in conceptually plausible directions. Using the same polythetic classification techniques as pathological social media use studies, we were able to classify 69% of our sample as addicted to spending time with their friends. The discussion of our satirical research is a critical reflection on the role of measurement and human sociality in social media research. We question the extent to which connecting with others can be considered an 'addiction' and discuss issues concerning the validation of new 'addiction' measures without relevant medical constructs. Readers should approach our measure with a level of skepticism that should be afforded to current social media addiction measures.

Characterization of Anorexia Nervosa on Social Media: Textual, Visual, Relational, Behavioral, and Demographical Analysis

BACKGROUND

Eating disorders are psychological conditions characterized by unhealthy eating habits. Anorexia nervosa (AN) is defined as the belief of being overweight despite being dangerously underweight. The psychological signs involve emotional and behavioral issues. There is evidence that signs and symptoms can manifest on social media, wherein both harmful and beneficial content is shared daily.

OBJECTIVE

This study aims to characterize Spanish-speaking users showing anorexia signs on Twitter through the extraction and inference of behavioral, demographical, relational, and multimodal data. By using the transtheoretical model of health behavior change, we focus on characterizing and comparing users at the different stages of the model for overcoming AN, including treatment and full recovery periods.

METHODS

We analyzed the writings, posting patterns, social relationships, and images shared by Twitter users who underwent different stages of anorexia nervosa and compared the differences among users going through each stage of the illness and users in the control group (ie, users without AN). We also analyzed the topics of interest of their followees (ie, users followed by study participants). We used a clustering approach to distinguish users at an early phase of the illness (precontemplation) from those that recognize that their behavior is problematic (contemplation) and generated models for the detection of tweets and images related to AN. We considered two types of control users-focused control users, which are those that use terms related to anorexia, and random control users.

RESULTS

We found significant differences between users at each stage of the recovery process (P<.001) and control groups. Users with AN tweeted more frequently at night, with a median sleep time tweets ratio (STTR) of 0.05, than random control users (STTR=0.04) and focused control users (STTR=0.03). Pictures were relevant for the characterization of users. Focused and random control users were characterized by the use of text in their profile pictures. We also found a strong polarization between focused control users and users in the first stages of the disorder. There was a strong correlation among the shared interests between users with AN and their followees (ρ=0.96). In addition, the interests of recovered users and users in treatment were more highly correlated to those corresponding to the focused control group (ρ=0.87 for both) than those of AN users (ρ=0.67), suggesting a shift in users' interest during the recovery process.

CONCLUSIONS

We mapped the signs of AN to social media context. These results support the findings of previous studies that focused on other languages and involved a deep analysis of the topics of interest of users at each phase of the disorder. The features and patterns identified provide a basis for the development of detection tools and recommender systems.

Examining the Pro-Eating Disorders Community on Twitter Via the Hashtag #proana: Statistical Modeling Approach

BACKGROUND

There is increasing concern around communities that promote eating disorders (Pro-ED) on social media sites through messages and images that encourage dangerous weight control behaviors. These communities share group identity formed through interactions between members and can involve the exchange of "tips," restrictive dieting plans, extreme exercise plans, and motivating imagery of thin bodies. Unlike Instagram, Facebook, or Tumblr, the absence of adequate policy to moderate Pro-ED content on Twitter presents a unique space for the Pro-ED community to freely communicate. While recent research has identified terms, themes, and common lexicon used within the Pro-ED online community, very few have been longitudinal. It is important to focus upon the engagement of Pro-ED online communities over time to further understand how members interact and stay connected, which is currently lacking.

OBJECTIVE

The purpose of this study was to explore beyond the common messages of Pro-ED on Twitter to understand how Pro-ED communities get traction over time by using the hashtag considered to symbolize the Pro-ED movement, #proana. Our focus was to collect longitudinal data to gain a further understanding of the engagement of Pro-ED communities on Twitter.

METHODS

Descriptive statistics were used to identify the preferred tweeting style of Twitter users (either as mentioning another user in a tweet or without) as well as their most frequently used hashtag, in addition to #proana. A series of Mann Whitney U tests were then conducted to compare preferred posting style across number of followed, followers, tweets, and favorites. This was followed by linear models using a forward step-wise approach that were applied for Pro-ED Twitter users to examine the factors associated with their number of followers.

RESULTS

This study reviewed 11,620 Pro-ED Twitter accounts that posted using the hashtag #proana between September 2015 and July 2018. These profiles then underwent a 2-step screening of inclusion and exclusion criteria to reach the final sample of 967 profiles. Over 90% (10,484/11,620) of the profiles were found to have less than 6 tweets within the 34-month period. Most of the users were identified as preferring a mentioning style of tweeting (718/967, 74.3%) over not mentioning (248/967, 25.7%). Further, #proana and #thinspo were used interchangeably to propagate shared themes, and there was a reciprocal effect between followers and the followed.

CONCLUSIONS

Our analysis showed that the number of accounts followed and number of Pro-ED tweets posted were significant predictors for the number of followers a user has, compared to likes. Our results could potentially be useful to social media platforms to understand which features could help or otherwise curtail the spread of ED messages and activity. Our findings also show that Pro-ED communities are transient in nature, engaging in superficial discussion threads but resilient, emulating cybersectarian behavior.

An examination of social group memberships in patients with eating disorders, carers, and healthy controls

This study compared the quantity and quality of social group memberships in patients with anorexia nervosa (n = 30), carers of patients with anorexia nervosa, unrelated to those patients (n = 30), and two cohorts of healthy controls (n = 60) age-matched to these focal groups. A secondary aim was to examine the associations between the quality of group relationships and severity of eating disorder and depression symptoms in patients; and depression symptoms in carers. Participants completed the online Social Identity Mapping Tool, which was used to measure the quantity and quality of social group memberships (e.g., number of social groups, number of groups rated 'highly positive'). Participants also completed self-report measures of clinical symptoms. Compared to controls, patients reported fewer social groups when eating disorder-related groups were included, and significantly fewer social groups, and community groups in particular, when eating disorder-related groups were excluded. Number of positive groups was negatively associated with severity of eating disorder and depression symptoms in patients when eating disorder-related groups were excluded. Carers reported fewer groups overall, fewer family groups, and fewer positive and supportive groups compared to healthy controls. There was a weak association between the number of positive groups and the severity of depression symptoms in carers. Positive group memberships might play a protective role towards developing more severe eating disorder and depression symptoms.

Use of social media data for disease based social network analysis and network modeling: A Systematic Review

Burden due to infectious and noncommunicable disease is increasing at an alarming rate. Social media usage is growing rapidly and has become the new norm of communication. It is imperative to examine what is being discussed in the social media about diseases or conditions and the characteristics of the network of people involved in discussion. The objective is to assess the tools and techniques used to study social media disease networks using network analysis and network modeling. PubMed and IEEEXplore were searched from 2009 to 2020 and included 30 studies after screening and analysis. Twitter, QuitNet, and disease-specific online forums were widely used to study communications on various health conditions. Most of the studies have performed content analysis and network analysis, whereas network modeling has been done in six studies. Posts on cancer, COVID-19, and smoking have been widely studied. Tools and techniques used for network analysis are listed. Health-related social media data can be leveraged for network analysis. Network modeling technique would help to identify the structural factors associated with the affiliation of the disease networks, which is scarcely utilized. This will help public health professionals to tailor targeted interventions.

Characterizing a body positive online forum: Resistance and pursuit of appearance-ideals

The body positive movement emerged in response to pressure to pursue the unattainable thin ideal, and promotes a more accepting stance towards the body. To date, however, little is known regarding the nature of online body positive content. Using publicly available data from a large online discussion platform (Reddit), all the forums to which commenters on a body acceptance forum (N = 1262) had also contributed were identified. For each pairing of 50 representative forums (i.e., a large number and proportion of body acceptance commenters), the commenter-overlap between the two forums was used to compute a network model, to detect communities of body acceptance commenters. By manually reviewing the topics of each community's forums, the shared interests of these commenters were identified. The majority of commenters (86 %) contributed to forums relating to women, feminism, relationships and support, and mental health. Large proportions of the commenters also revealed an interest in topics including body weight/shape, eating, exercise, and cosmetics. These findings confirm that original feminist tenets of body positivity remain present. However, our findings also suggest the existence of a sizeable subgroup interacting with topics related to the thin ideal, perhaps illustrating a gradual absorption of the body positive movement into mainstream culture.

Effects of user behaviors on accumulation of social capital in an online social network

The use of social network sites helps people to make and maintain social ties accumulating social capital, which is increasingly important for individual success. There is a wide variation in the amount and structure of online ties, and to some extent this variation is contingent on specific online user behaviors which are to date under-researched. In this work, we examine an entire city-bounded friendship network (N = 194,601) extracted from VK social network site to explore how specific online user behaviors are related to structural social capital in a network of geographically proximate ties. Social network analysis was used to evaluate individual social capital as a network asset, and multiple regression analysis–to determine and estimate the effects of online user behaviors on social capital. The analysis reveals that the graph is both clustered and highly centralized which suggests the presence of a hierarchical structure: a set of sub-communities united by city-level hubs. Against this background, membership in more online groups is positively associated with user’s brokerage in the location-bounded network. Additionally, the share of local friends, the number of received likes and the duration of SNS use are associated with social capital indicators. This contributes to the literature on the formation of online social capital, examined at the level of a large and geographically localized population.

What Twitter teaches us about patient-provider communication on pain

The objective of this study was to understand how pain patients and health care providers (HCPs) are engaging on Twitter and what insights this engagement might provide. By identifying how information is spread by and between these audiences, organizations such as patient advocacy groups may be better poised to develop and share materials that facilitate online communication between HCPs and pain patients, with an end goal of improving a shared decision-making process around pain management. We analyzed the Twitter audiences most engaged on pain topics by conducting a Social Network Analysis (SNA) of a large network of connected users on Twitter. The analysis segmented users based on the sources they cited and measured their influence based on who follows them. As a point of comparison, we also conducted an SNA of Twitter audiences most engaged on oncology topics. Oncology was chosen as a comparison due to what was perceived to be a highly developed online network of both patients and physicians. The populations included in this research included 12,086 accounts that were highly engaged on pain-related topics, and 12,617 accounts that were highly engaged on oncology-related topics. Network statistics were generated for variables including: word use, sources cited, retweets, and mentions. We also statistically analyzed the Twitter follow relationships among select HCPs and patient groups within each SNA. The creation of separate pain and oncology SNAs allowed the team to compare relationships and engagement related to these topics. We found that on Twitter, pain patients and providers appear to interact less than oncology patients and providers. Pain patients do not appear to follow medical professionals or share medical or health-related information on Twitter to the same extent as oncology patients. In addition, we found that pain patients do not communicate on Twitter in the same language as HCPs. Our results are important because they underscore that challenges in communication are not just problematic in face-to-face interactions, but also in digital social network (Twitter) interactions, serving as an additional roadblock to what can be shared decision-making opportunities around pain management. Contributing to this roadblock is access to quality information and a potential need for an online, evidence-based resource hub that could benefit the pain patient community in the same way that cancer.gov serves as a source of aggregated materials for oncology patients and HCPs. This study is an illustration of how social media networks like Twitter can be used to better understand the relationships, language gaps, and shared resources between pain patients and providers and offers a template for using digital social network (Twitter) interactions to research other difficult-to-treat or rare disease states.

The evolution of online ideological communities

Online communities are virtual spaces for users to share interests, support others, and to exchange knowledge and information. Understanding user behavior is valuable to organizations and has applications from marketing to security, for instance, identifying leaders within a community or predicting future behavior. In the present research, we seek to understand the various roles that users adopt in online communities–for instance, who leads the conversation? Who are the supporters? We examine user role changes over time and the pathways that users follow. This allows us to explore the differences between users who progress to leadership positions and users who fail to develop influence. We also reflect on how user role proportions impact the overall health of the community. Here, we examine two online ideological communities, RevLeft and Islamic Awakening (N = 1631; N = 849), and provide a novel approach to identify various types of users. Finally, we study user role trajectories over time and identify community “leaders” from meta-data alone. Study One examined both communities using K-MEANS cluster analysis of behavioral meta-data, which revealed seven user roles. We then mapped these roles against Preece and Schneiderman’s (2009) Reader-to-Leader Framework (RtLF). Both communities aligned with the RtLF, where most users were “contributors”, many were “collaborators”, and few were “leaders”. Study Two looked at one community over a two-year period and found that, despite a high churn rate of users, roles were stable over time. We built a model of user role transitions over the two years. This can be used to predict user role changes in the future, which will have implications for community managers and security focused contexts (e.g., analyzing behavioral meta-data from forums and websites known to be associated with illicit activity).

Profiling Commenters on Mental Health-Related Online Forums: A Methodological Example Focusing on Eating Disorder-Related Commenters

BACKGROUND

Understanding the characteristics of commenters on mental health-related online forums is vital for the development of effective psychological interventions in these communities. The way in which commenters interact can enhance our understanding of their characteristics.

OBJECTIVE

Using eating disorder-related (EDR) forums as an example, this study detailed a methodology that aimed to determine subtypes of mental health-related forums and profile their commenters based on the other forums to which they contributed.

METHODS

The researchers identified all public EDR forums (with ≥500 contributing commenters between March 2017 and February 2018) on a large Web-based discussion platform (Reddit). A mixed-methods approach comprising network analysis with community detection, text mining, and manual review identified subtypes of EDR forums. For each subtype, another network analysis with community detection was conducted using the EDR forum commenter overlap between 50 forums on which the commenters also commented. The topics of forums in each detected community were then manually reviewed to identify the shared interests of each subtype of EDR forum commenters.

RESULTS

Six subtypes of EDR forums were identified, to which 14,024 commenters had contributed. The results focus on 2 subtypes-proeating disorder and thinspiration-and communities of commenters within both subtypes. Within the proeating disorder subtype, 3 communities of commenters were detected that related to the body and eating, mental health, and women, appearance, and mixed topics. With regard to the thinspiration group, 78.17% (849/1086) of commenters had also commented on pornographic forums and 16.66% (181/1086) had contributed to proeating disorder forums.

CONCLUSIONS

The article exemplifies a methodology that provides insight into subtypes of mental health-related forums and the characteristics of their commenters. The findings have implications for future research and Web-based psychological interventions. With the publicly available data and code provided, researchers can easily reproduce the analyses or utilize the methodology to investigate other mental health-related forums.