Elsevier

Computer Communications

Volume 73, Part B, 1 January 2016, Pages 291-300
Computer Communications

Analysing the connectivity and communication of suicidal users on twitter

https://doi.org/10.1016/j.comcom.2015.07.018Get rights and content
Under a Creative Commons license
open access

Highlights

  • We investigate the characteristics of the authors of Tweets containing suicidal intent or thinking, through the analysis of their online social network relationships and interactions.

  • 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.

  • Analysis of the retweet graph identified bridge nodes and hub nodes connecting users posting suicidal ideation with users who were not, suggesting a potential for information cascade and risk of possible ‘contagion’.

  • Retweet graphs of suicidal content exhibit an average shortest path similar to that of a large comparison network, demonstrating large scale information propagation in small-scale networks.

Abstract

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 posting 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.

Keywords

Social media
Social network analysis
Twitter
Computational social science
Suicide

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