Helping or Hurting? Predicting Changes in Users’ Risk of Self-Harm
            Through Online Community Interactions

Soldaini, Luca; Walsh, Timothy; Cohan, Arman; Han, Julien; Goharian, Nazli

doi:10.18653/v1/w18-0621

Cited by 18 publications

(18 citation statements)

References 31 publications

(24 reference statements)

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“…ble-edged sword of online peer support has been the focus for several studies of online forums, particularly on suicide prevention forums. Here, the ability to provide immediate support to individuals in moments of crisis are essential to prevent suicide, but among the thousands of posts on such forums users can also come to normalize harmful behavior, discour age professional treatment, and instigate suicidal ideation (Soldaini et al, 2018). Using social media for peer support can thus have the opposite effect to a desired sense of recovery, largely because of the potential risk of "contagion" -in the case of suicide prevention forums this means enhanc ing the risk of people copying acts of suicide (Robinson et al, 2015).…”

Section: Mikkel Rask Pedersenmentioning

confidence: 99%

The perspective of victims/survivors on media coverage of child sexual abuse

Nagel,

Kavemann

2022

Child Sexual Abuse and the Media

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Section: Mikkel Rask Pedersenmentioning

confidence: 99%

The perspective of victims/survivors on media coverage of child sexual abuse

Nagel,

Kavemann

2022

Child Sexual Abuse and the Media

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“…Previous studies have shown that behavioral patterns and linguistic features of posts in web-based communities can distinguish between people with and without a number of conditions [ 56 - 58 ] and can be used to infer risk [ 59 , 60 ]. Much of these studies have combined behavioral measures such as posting frequency, with linguistic features such as language use or themes embedded in published content.…”

Section: Introductionmentioning

confidence: 99%

Examining the Relationship Between the Use of a Mobile Peer-Support App and Self-Injury Outcomes: Longitudinal Mixed Methods Study

Kruzan¹,

Whitlock²,

Bazarova³

2021

JMIR Ment Health

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Background Many individuals who self-injure seek support and information through online communities and mobile peer-support apps. Although researchers have identified risks and benefits of participation, empirical work linking participation in these web-based spaces to self-injury behaviors and thoughts is limited. Objective This study aims to investigate the relationship between behavioral and linguistic traces on a mobile peer support app and self-injury outcomes. Methods Natural use data and web-based surveys (N=697) assessing self-injury outcomes were collected from 268 users (aged 13-38 years; median 19; 149/268, 55.6% female) of a mobile peer-support app for 4 months. Participants were identified as having posted self-injury content using an internal classifier. Natural log data was used to predict self-injury outcomes in a series of multilevel logistic and linear regressions. Results Greater engagement on a mobile peer-support app was associated with a decreased likelihood of self-injury thoughts (odds ratio [OR] 0.25, 95% CI 0.09-0.73) and fewer intentions to self-injure (b=−0.37, SE 0.09), whereas posting triggering content was associated with an increased likelihood of engaging in behaviors (OR 5.37, 95% CI 1.25-23.05) and having self-injury thoughts (OR 17.87, 95% CI 1.64-194.15). Moreover, viewing triggering content was related to both a greater ability to resist (b=1.39, SE 0.66) and a greater intention to self-injure (b=1.50, SE 0.06). Conclusions To our knowledge, this is the first study to connect naturally occurring log data to survey data assessing self-injury outcomes over time. This work provides empirical support for the relationship between participation in online forums and self-injury outcomes, and it articulates mechanisms contributing to this relationship.

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“…Pennebaker et al (2015) perform one of the most influential quantitative studies, which reveals the way patterns of parts of speech, as labelled by LIWC founders, correlate with types of personalities and types of mental illnesses. The classes and the psychological dimensions mapped together served as a start for many projects including the prediction of Dark Triad personality traits by Sumner et al (2012) and the risk of selfharm by Soldaini et al (2018). Research in the area is conducted mainly on texts from mental health support groups, on just a few illnesses and some groups of individuals.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection and Classification of Mental Illnesses from General Social Media Texts

Dinu

Moldovan²

2021

Proceedings of the Conference Recent Advances in Natural Language Processing - Deep Learning for Natural Language Processing Me

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Mental health is getting more and more attention recently, depression being a very common illness nowadays, but also other disorders like anxiety, obsessive-compulsive disorders, feeding disorders, autism, or attention-deficit/hyperactivity disorders. The huge amount of data from social media and the recent advances of deep learning models provide valuable means to automatically detecting mental disorders from plain text. In this article, we experiment with state-of-the-art methods on the SMHD mental health conditions dataset from Reddit (Cohan et al., 2018). Our contribution is threefold: using a dataset consisting of more illnesses than most studies, focusing on general text rather than mental health support groups and classification by posts rather than individuals or groups. For the automatic classification of the diseases, we employ three deep learning models: BERT, RoBERTa and XLNET. We double the baseline established by Cohan et al. (2018), on just a sample of their dataset. We improve the results obtained by Jiang et al. ( 2020) on post-level classification. The accuracy obtained by the eating disorder classifier is the highest due to the pregnant presence of discussions related to calories, diets, recipes etc., whereas depression had the lowest F1 score, probably because depression is more difficult to identify in linguistic acts. UEFISCDI, project number 108, COTOHILI, within PNCDI III.

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Helping or Hurting? Predicting Changes in Users’ Risk of Self-Harm Through Online Community Interactions

Cited by 18 publications

References 31 publications

The perspective of victims/survivors on media coverage of child sexual abuse

The perspective of victims/survivors on media coverage of child sexual abuse

Examining the Relationship Between the Use of a Mobile Peer-Support App and Self-Injury Outcomes: Longitudinal Mixed Methods Study

Automatic Detection and Classification of Mental Illnesses from General Social Media Texts

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