Semantic networks of interests in online non-suicidal self-injury communities (original) (raw)
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Semantic Networks of Interests in Online NSSI Communities
Persons who engage in non-suicidal self-injury (NSSI), often conceal their practices which limits the examination and understanding of those who engage in NSSI. The goal of this research is to utilize public online social networks (namely, in LiveJournal, a major blogging network) to observe the NSSI population’s communication in a naturally occurring setting. Specifically, LiveJournal users can publicly declare their interests. We collected the self-declared interests of 22,000 users who are members of or participate in 43 NSSI-related communities. We extracted a bimodal socio-semantic network of users and interests based on their similarity. The semantic subnetwork of interests contains NSSI terms (such as "self-injury" and "razors"), references to music performers (such as "Nine Inch Nails"), and general daily life and creativity related terms (such as "poetry" and "boys"). Assuming users are genuine in their declarations, the words reveal distinct patterns of interest and may signal keys to NSSI.
DIGITAL HEALTH, 2020
Objectives #selfharm has been blocked by Instagram, but manoeuvring hashtags (e.g. #selfharn) are beginning to appear in order for secret non-suicidal self-injury (NSSI) communities to communicate. The purpose of this study was to (a) determine the nature of the #selfharn conversation on Instagram, (b) analyze common properties of the visual content (i.e. images and videos; n = 93) tagged with #selfharn, and (c) discover what kind of environment the authors ( n = 50) of #selfharn were creating. Methods A multi-method approach was utilized for this study. Netlytic was used to generate a text and content analysis to examine the authors’ captions and comments ( n = 8772) associated with #selfharn (collected over a seven-day period). Results After removing #selfharn from the dataset, the text analysis revealed that #depression ( n = 3081) and #suicide ( n = 2270) were the most commonly used terms associated with #selfharn. Overall, 52% ( n = 4386) of the popular words/phrases related wi...
Analysing the connectivity and communication of suicidal users on Twitter
In this paper we aim to understand the connectivity and communication characteristics of Twitter users who post content subsequently classified by human annotators as containing possible suicidal intent or thinking, commonly referred to as suicidal ideation. We achieve this understanding by analysing the characteristics of their social networks. Starting from a set of human annotated Tweets we retrieved the authors’ followers and friends lists, and identified users who retweeted the suicidal content. We subsequently built the social network graphs. Our results show a high degree of reciprocal connectivity between the authors of suicidal content when compared to other studies of Twitter users, suggesting a tightly-coupled virtual community. In addition, an analysis of the retweet graph has identified bridge nodes and hub nodes connecting users post- ing suicidal ideation with users who were not, thus suggesting a potential for information cascade and risk of a possible contagion effect. This is particularly emphasised by considering the combined graph merging friendship and retweeting links
Self-harm: detection and support on Twitter
2021
Since the advent of online social media platforms such as Twitter and Facebook, useful health-related studies have been conducted using the information posted by online participants. Personal health-related issues such as mental health, self-harm and depression have been studied because users often share their stories on such platforms. Online users resort to sharing because the empathy and support from online communities are crucial in helping the affected individuals. A preliminary analysis shows how contents related to non-suicidal self-injury (NSSI) proliferate on Twitter. Thus, we use Twitter to collect relevant data, analyse, and proffer ways of supporting users prone to NSSI behaviour. Our approach utilises a custom crawler to retrieve relevant tweets from self-reporting users and relevant organisations interested in combating self-harm. Through textual analysis, we identify six major categories of self-harming users consisting of inflicted, anti-self-harm, support seekers, r...
Comparing Suicide Risk Insights derived from Clinical and Social Media data
2020
Suicide is the 10 leading cause of death in the US and the 2 leading cause of death among teenagers. Clinical and psychosocial factors contribute to suicide risk (SRFs), although documentation and self-expression of such factors in EHRs and social networks vary. This study investigates the degree of variance across EHRs and social networks. We performed subjective analysis of SRFs, such as self-harm, bullying, impulsivity, family violence/discord, using >13.8 Million clinical notes on 123,703 patients with mental health conditions. We clustered clinical notes using semantic embeddings under a set of SRFs. Likewise, we clustered 2180 suicidal users on r/SuicideWatch (∼30,000 posts) and performed comparative analysis. Top-3 SRFs documented in EHRs were depressive feelings (24.3%), psychological disorders (21.1%), drug abuse (18.2%). In r/SuicideWatch, gun-ownership (17.3%), self-harm (14.6%), bullying (13.2%) were Top-3 SRFs. Mentions of Family violence, racial discrimination, and ...
ArXiv, 2021
Writing messages is key to expressing feelings. This study adopts cognitive network science to reconstruct how individuals report their feelings in clinical narratives like suicide notes or mental health posts. We achieve this by reconstructing syntactic/semantic associations between concepts in texts as co-occurrences enriched with affective data. We transform 142 suicide notes and 77,000 Reddit posts from the r/anxiety, r/depression, r/schizophrenia, and r/do-it-your-own (r/DIY) forums into 5 cognitive networks, each one expressing meanings and emotions as reported by authors. These networks reconstruct the semantic frames surrounding "feel", stem for "to feel" and "feelings", enabling a quantification of prominent associations and emotions focused around feelings. We find strong feelings of sadness across all clinical Reddit boards, added to fear r/depression, and replaced by joy/anticipation in r/DIY. Semantic communities and topic modelling both hi...
Quantifying the Suicidal Tendency on Social Media: A Survey
ArXiv, 2021
Amid lockdown period more people express their feelings over social media platforms due to closed third-place and academic researchers have witnessed strong associations between the mental healthcare and social media posts. The stress for a brief period may lead to clinical depressions and the long-lasting traits of prevailing depressions can be life threatening with suicidal ideation as the possible outcome. The increasing concern towards the rise in number of suicide cases is because it is one of the leading cause of premature but preventable death. Recent studies have shown that mining social media data has helped in quantifying the suicidal tendency of users at risk. This potential manuscript elucidates the taxonomy of mental healthcare and highlights some recent attempts in examining the potential of quantifying suicidal tendency on social media data. This manuscript presents the classification of heterogeneous features from social media data and handling feature vector represe...
Suicide ideation of individuals in online social networks
PloS one, 2013
Suicide explains the largest number of death tolls among Japanese adolescents in their twenties and thirties. Suicide is also a major cause of death for adolescents in many other countries. Although social isolation has been implicated to influence the tendency to suicidal behavior, the impact of social isolation on suicide in the context of explicit social networks of individuals is scarcely explored. To address this question, we examined a large data set obtained from a social networking service dominant in Japan. The social network is composed of a set of friendship ties between pairs of users created by mutual endorsement. We carried out the logistic regression to identify users' characteristics, both related and unrelated to social networks, which contribute to suicide ideation. We defined suicide ideation of a user as the membership to at least one active user-defined community related to suicide. We found that the number of communities to which a user belongs to, the intr...
Suicide and Life-Threatening Behavior, 2019
Self-injurious thoughts and behaviors (SITBs) are a complex and enduring public health concern. Increasingly, teenagers use digital platforms to communicate about a range of mental health topics. These discussions may provide valuable information that can lead to insights about complex issues like SITBs. However, the field of clinical psychology currently lacks an easy-to-implement toolkit that can quickly gather information about SITBs from online sources. In the present study, we applied topic modeling, a natural language processing technique, to identify SITBs and related themes online, and we validated this approach using human coders. Method: We separately used topic modeling software and human coders to identify themes present in text from a popular online Internet support forum for teenagers. We then determined the degree to which results from the software's topic model aligned with themes identified by human coders. Results: We found that topic modeling detected SITBs and related themes in online discussions in a way that accurately distinguishes between relevant and irrelevant human-coded themes. Conclusions: This approach has the potential to drastically increase our understanding of SITBs and related issues discussed on digital platforms, as well as our ability to identify those at risk for such outcomes. Self-injurious thoughts and behaviors (SITBs; including both suicidal thoughts/behaviors and nonsuicidal self-injury [NSSI]) are a major health concern worldwide. In the United States, suicide is the second leading cause of death among adolescents and young adults aged 15-34 (CDC, 2017). However, many people considering suicide do not disclose, or outright deny, having suicidal thoughts to others (Busch, Fawcett, & Jacobs, 2003). As a result, it is extremely difficult to identify those who may be at risk for SITBs. Prior studies suggest that those engaging in SITBs might be more likely to discuss these matters in Internet communities than with mental health professionals, potentially because these communities are less stigmatizing than other sources of support (Burns,
On the Creative Edge: Exploring Motivations for Creating Non-Suicidal Self-Injury Content Online
The last decade has witnessed an exponential growth in user-generated online content featuring Non-Suicidal Self- Injury (NSSI), including photography, digital video, poems, blogging, and drawings. Although the increasing visibility of NSSI content has evoked public concern over potential health risks, little research has investigated why people are drawn to create and publish such content. This article reports the findings from a qualitative analysis of online interviews with 17 individuals who produce NSSI content. A thematic analysis of participants’ narratives identified two prominent motives: self-oriented motivation (to express self and creativity, to reflect on NSSI experience, to mitigate self-destructive urges) and social motivation (to support similar others, to seek out peers, to raise social awareness). Participants also reported a double-edged impact of NSSI content both as a trigger and a deterrent to NSSI.