Attitudes towards suicide attempts broadcast on social media: an exploratory study of Chinese microblogs

Abstract

Introduction. Broadcasting a suicide attempt on social media has become a public health concern in many countries, particularly in China. In these cases, social media users are likely to be the first to witness the suicide attempt, and their attitudes may determine their likelihood of joining rescue efforts. This paper examines Chinese social media (Weibo) users’ attitudes towards suicide attempts broadcast on Weibo.Methods. A total of 4,969 Weibo posts were selected from a customised Weibo User Pool which consisted of 1.06 million active users. The selected posts were then independently coded by two researchers using a coding framework that assessed: (a) Themes, (b) General attitudes, (c) Stigmatising attitudes, (d) Perceived motivations, and (e) Desired responses.Results and Discussion. More than one third of Weibo posts were coded as “stigmatising” (35%). Among these, 22%, 16%, and 15% of posts were coded as “deceitful,” “pathetic,” and “stupid,” respectively. Among the posts which reflected different types of perceived motivations, 57% of posts were coded as “seeking attention.” Among the posts which reflected desired responses, 37% were “not saving” and 28% were “encouraging suicide.” Furthermore, among the posts with negative desired responses (i.e., “not saving” and “encouraging suicide”), 57% and 17% of them were related to different types of stigmatising attitudes and perceived motivations, respectively. Specifically, 29% and 26% of posts reflecting both stigmatising attitudes and negative desired responses were coded as “deceitful” and “pathetic,” respectively, while 66% of posts reflecting both perceived motivations, and negative desired responses were coded as “seeking attention.” Very few posts “promoted literacy” (2%) or “provided resources” (8%). Gender differences existed in multiple categories.Conclusions. This paper confirms the need for stigma reduction campaigns for Chinese social media users to improve their attitudes towards those who broadcast their suicide attempts on social media. Results of this study support the need for improved public health programs in China and may be insightful for other countries and other social media platforms.

An analysis of stigma and suicide literacy in responses to suicides broadcast on social media

“…After collecting the raw data, preprocessing was conducted prior to further analysis (Li et al, ). To obtain the predicted class labels (eg, “stigmatizing” or “nonstigmatizing”), 2 independent coders conducted a content analysis on the collected posts.…”

“…Evidence suggests that stigma is prominent among social media users' responses to such a broadcast, hindering the early recognition and appropriate treatment of suicide, and even escalating outcomes. Moreover, in online attempts, the misleading belief that a suicide attempt on social media is not genuine (ie, false representation stigma) appears to manifest more frequently than any other types of stigmatizing attitudes (Li et al, ). This disbelief can further reduce the likelihood that the online audience will respond appropriately.…”

Natural language processing in mental health applications using non-clinical texts

“…Nguyen et al (2014) separate out LiveJournal posts that discuss depression and related topics. Homan et al (2014) and O'Dea et al (2015) detect posts containing suicide ideation and distress, and Li et al (2015) investigate unhelpful, stigmatizing reactions to suicide on the Chinese social media platform Weibo. Milne et al (2016) host a shared task for identifying and prioritizing concerning content on ReachOut.com's peer support forum.…”

Suicide, Stigma, and Utilizing Social Media Platforms to Gauge Public Perceptions

“…Because of this, we need to harness technological advances such as machine learning to create safer spaces on social media platforms for those at risk for suicidality. Linguistic pattern recognition of stigma expressions around suicide attempts in Weibo (social media in China) posts have confirmed that social media mining can help improve stigma reduction programs (48). Machine learning is currently used to automatically identify and score helpful comments in a subreddit suicide watch forum to assist moderators with immediate feedback for help with online suicide prevention (49).…”