Literature DB >> 35224567

It's quality and quantity: the effect of the amount of comments on online suicidal posts.

Daniel M Low1, Kelly L Zuromski2, Daniel Kessler2, Satrajit S Ghosh3, Matthew Nock2, Walter Dempsey4.   

Abstract

Every day, individuals post suicide notes on social media asking for support, resources, and reasons to live. Some posts receive few comments while others receive many. While prior studies have analyzed whether specific responses are more or less helpful, it is not clear if the quantity of comments received is beneficial in reducing symptoms or in keeping the user engaged with the platform and hence with life. In the present study, we create a large dataset of users' first r/SuicideWatch (SW) posts from Reddit (N=21,274), collect the comments as well as the user's subsequent posts (N=1,615,699) to determine whether they post in SW again in the future. We use propensity score stratification, a causal inference method for observational data, and estimate whether the amount of comments -as a measure of social support- increases or decreases the likelihood of posting again on SW. One hypothesis is that receiving more comments may decrease the likelihood of the user posting in SW in the future, either by reducing symptoms or because comments from untrained peers may be harmful. On the contrary, we find that receiving more comments increases the likelihood a user will post in SW again. We discuss how receiving more comments is helpful, not by permanently relieving symptoms since users make another SW post and their second posts have similar mentions of suicidal ideation, but rather by reinforcing users to seek support and remain engaged with the platform. Furthermore, since receiving only 1 comment -the most common case- decreases the likelihood of posting again by 14% on average depending on the time window, it is important to develop systems that encourage more commenting.

Entities:  

Year:  2021        PMID: 35224567      PMCID: PMC8880842          DOI: 10.18653/v1/2021.cinlp-1.8

Source DB:  PubMed          Journal:  Proc Conf Empir Methods Nat Lang Process


  21 in total

1.  Randomized controlled trial of an online machine learning-driven risk assessment and intervention platform for increasing the use of crisis services.

Authors:  Adam C Jaroszewski; Robert R Morris; Matthew K Nock
Journal:  J Consult Clin Psychol       Date:  2019-04

2.  Involving service users in intervention design: a participatory approach to developing a text-messaging intervention to reduce repetition of self-harm.

Authors:  Christabel Owens; Paul Farrand; Ruth Darvill; Tobit Emmens; Elaine Hewis; Peter Aitken
Journal:  Health Expect       Date:  2010-09-23       Impact factor: 3.377

3.  Perceived barriers to psychological treatments and their relationship to depression.

Authors:  David C Mohr; Joyce Ho; Jenna Duffecy; Kelly G Baron; Kenneth A Lehman; Ling Jin; Douglas Reifler
Journal:  J Clin Psychol       Date:  2010-04

4.  The future of mental health care: peer-to-peer support and social media.

Authors:  J A Naslund; K A Aschbrenner; L A Marsch; S J Bartels
Journal:  Epidemiol Psychiatr Sci       Date:  2016-01-08       Impact factor: 6.892

5.  Barriers to mental health treatment: results from the WHO World Mental Health surveys.

Authors:  L H Andrade; J Alonso; Z Mneimneh; J E Wells; A Al-Hamzawi; G Borges; E Bromet; R Bruffaerts; G de Girolamo; R de Graaf; S Florescu; O Gureje; H R Hinkov; C Hu; Y Huang; I Hwang; R Jin; E G Karam; V Kovess-Masfety; D Levinson; H Matschinger; S O'Neill; J Posada-Villa; R Sagar; N A Sampson; C Sasu; D J Stein; T Takeshima; M C Viana; M Xavier; R C Kessler
Journal:  Psychol Med       Date:  2013-08-09       Impact factor: 7.723

6.  Implementing an evidence-based psychological intervention for suicidal thoughts and behaviors on an inpatient unit: Process, challenges, and initial findings.

Authors:  Kate H Bentley; Shannon Sauer-Zavala; Kimberly T Stevens; Jason J Washburn
Journal:  Gen Hosp Psychiatry       Date:  2018-09-27       Impact factor: 3.238

7.  Designing messaging to engage patients in an online suicide prevention intervention: survey results from patients with current suicidal ideation.

Authors:  Ursula Whiteside; Anita Lungu; Julie Richards; Gregory E Simon; Sarah Clingan; Jaeden Siler; Lorilei Snyder; Evette Ludman
Journal:  J Med Internet Res       Date:  2014-02-07       Impact factor: 5.428

8.  Mobile Phone Intervention to Reduce Youth Suicide in Rural Communities: Field Test.

Authors:  Anthony R Pisani; Peter A Wyman; Kunali Gurditta; Karen Schmeelk-Cone; Carolyn L Anderson; Emily Judd
Journal:  JMIR Ment Health       Date:  2018-05-31

9.  Analysing the connectivity and communication of suicidal users on twitter.

Authors:  Gualtiero B Colombo; Pete Burnap; Andrei Hodorog; Jonathan Scourfield
Journal:  Comput Commun       Date:  2016-01-01       Impact factor: 3.167

10.  The rate of reply and nature of responses to suicide-related posts on Twitter.

Authors:  Bridianne O'Dea; Melinda R Achilles; Mark E Larsen; Philip J Batterham; Alison L Calear; Helen Christensen
Journal:  Internet Interv       Date:  2018-07-19
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