Understanding Moderation in Online Mental Health Communities

Saha, Koustuv; Ernala, Sindhu Kiranmai; Dutta, Sarmistha; Sharma, Eva; Choudhury, Munmun De

doi:10.1007/978-3-030-49576-3_7

Cited by 30 publications

(26 citation statements)

References 66 publications

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“…In light of this research, our work could inform the design of tools to support the work of substance use related online community moderators. For instance, our results could inform the design of tools to help moderators target users posting risky content for interventional outreach, as discussed in recent studies on marginalized populations appropriating social media for health needs ( Andalibi et al, 2016 ; Saha et al, 2020 ; Wadden et al, 2021 ). Facebook similarly uses artificial intelligence (AI) to provide resources to those identified as being at risk for suicide based on a partnership with the National Suicide Prevention Lifeline ( Constine, 2017 ), and in March 2020 Reddit announced a partnership with Crisis Text Line to allow users to flag other users who may be in crisis ( Perez, 2020 ).…”

Section: Discussionmentioning

confidence: 79%

Detecting risk level in individuals misusing fentanyl utilizing posts from an online community on Reddit

Garg

Taylor

Sherief

et al. 2021

Internet Interventions

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Section: Discussionmentioning

confidence: 79%

Detecting risk level in individuals misusing fentanyl utilizing posts from an online community on Reddit

Garg

Taylor

Sherief

et al. 2021

Internet Interventions

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“…Although we cannot speculate on the exact reason why Twitter presented more misinformation in the case of OUD-related misinformation as that requires causal inference analysis, which is beyond the scope of this paper, prior literature has pointed out the lack of active expert or clinical-based moderation on Twitter [51]. Although web-based health communities are also not immune to bad behavior and antisocial activities such as trolling, spamming, and harassment, these communities are often guided by strict norms against such behavior and moderated to ensure the quality and credibility of the content being shared [52]. Prior studies on different types of web-based health communities have demonstrated that adequate active moderation increases the engagement of members and consequently also increases the beneficial outcomes for members in a web-based community [53].…”

Section: Principal Findingsmentioning

confidence: 99%

Characterizing and Identifying the Prevalence of Web-Based Misinformation Relating to Medication for Opioid Use Disorder: Machine Learning Approach

ElSherief¹,

Sumner²,

Jones³

et al. 2021

J Med Internet Res

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Background Expanding access to and use of medication for opioid use disorder (MOUD) is a key component of overdose prevention. An important barrier to the uptake of MOUD is exposure to inaccurate and potentially harmful health misinformation on social media or web-based forums where individuals commonly seek information. There is a significant need to devise computational techniques to describe the prevalence of web-based health misinformation related to MOUD to facilitate mitigation efforts. Objective By adopting a multidisciplinary, mixed methods strategy, this paper aims to present machine learning and natural language analysis approaches to identify the characteristics and prevalence of web-based misinformation related to MOUD to inform future prevention, treatment, and response efforts. Methods The team harnessed public social media posts and comments in the English language from Twitter (6,365,245 posts), YouTube (99,386 posts), Reddit (13,483,419 posts), and Drugs-Forum (5549 posts). Leveraging public health expert annotations on a sample of 2400 of these social media posts that were found to be semantically most similar to a variety of prevailing opioid use disorder–related myths based on representational learning, the team developed a supervised machine learning classifier. This classifier identified whether a post’s language promoted one of the leading myths challenging addiction treatment: that the use of agonist therapy for MOUD is simply replacing one drug with another. Platform-level prevalence was calculated thereafter by machine labeling all unannotated posts with the classifier and noting the proportion of myth-indicative posts over all posts. Results Our results demonstrate promise in identifying social media postings that center on treatment myths about opioid use disorder with an accuracy of 91% and an area under the curve of 0.9, including how these discussions vary across platforms in terms of prevalence and linguistic characteristics, with the lowest prevalence on web-based health communities such as Reddit and Drugs-Forum and the highest on Twitter. Specifically, the prevalence of the stated MOUD myth ranged from 0.4% on web-based health communities to 0.9% on Twitter. Conclusions This work provides one of the first large-scale assessments of a key MOUD-related myth across multiple social media platforms and highlights the feasibility and importance of ongoing assessment of health misinformation related to addiction treatment.

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“…Due to the lack of access to mental health services, individuals with mental health concerns are turning to online mental health communities such as Reddit to share their emotional challenges and seek social support. The goal of such communities is to provide a "safe haven" for mental health disclosure and peer-to-peer support for stigmatized concerns (Saha et al 2020). Previous research sug-gests that self-disclosure through anonymous communications may promote better mental health outcomes.…”

Section: Self-disclosure In Online Mental Health Communitiesmentioning

confidence: 99%

Many Ways to Be Lonely: Fine-Grained Characterization of Loneliness and Its Potential Changes in COVID-19

Jiang¹,

Yunfan²,

Leqi³

et al. 2022

ICWSM

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Loneliness has been associated with negative outcomes for physical and mental health. Understanding how people express and cope with various forms of loneliness is critical for early screening and targeted interventions to reduce loneliness, particularly among vulnerable groups such as young adults. To examine how different forms of loneliness and coping strategies manifest in loneliness self-disclosure, we built a dataset, FIG-Loneliness (FIne-Grained Loneliness) by using Reddit posts in two young adult-focused forums and two loneliness related forums consisting of a diverse age group. We provided annotations by trained human annotators for binary and fine-grained loneliness classifications of the posts. Trained on FIG-Loneliness, two BERT-based models were used to understand loneliness forms and authors’ coping strategies in these forums. Our binary loneliness classification achieved an accuracy above 97%, and fine-grained loneliness category classification reached an average accuracy of 77% across all labeled categories. With FIG-Loneliness and model predictions, we found that loneliness expressions in the young adult related forums were distinct from other forums. Those in young adult-focused forums were more likely to express concerns pertaining to peer relationship, and were potentially more sensitive to geographical isolation impacted by the COVID-19 pandemic lockdown. Also, we showed that different forms of loneliness have differential use in coping strategies.

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Understanding Moderation in Online Mental Health Communities

Cited by 30 publications

References 66 publications

Detecting risk level in individuals misusing fentanyl utilizing posts from an online community on Reddit

Detecting risk level in individuals misusing fentanyl utilizing posts from an online community on Reddit

Characterizing and Identifying the Prevalence of Web-Based Misinformation Relating to Medication for Opioid Use Disorder: Machine Learning Approach

Many Ways to Be Lonely: Fine-Grained Characterization of Loneliness and Its Potential Changes in COVID-19

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