title: Analysing the connectivity and communication of suicidal users on twitter.
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.
authors.affiliation: School of Computer Science and Informatics, Cardiff University, Queens Buildings, 5 The Parade, Cardiff, United Kingdom.
authors.forename: Gualtiero B
authors.initials: GB
authors.lastname: Colombo
authors.affiliation: School of Computer Science and Informatics, Cardiff University, Queens Buildings, 5 The Parade, Cardiff, United Kingdom.
authors.forename: Pete
authors.initials: P
authors.lastname: Burnap
authors.affiliation: School of Computer Science and Informatics, Cardiff University, Queens Buildings, 5 The Parade, Cardiff, United Kingdom.
authors.forename: Andrei
authors.initials: A
authors.lastname: Hodorog
authors.affiliation: School of Social Science, Cardiff University, Glamorgan Building, King Edward VII Avenue, Cardiff, United Kingdom.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Scourfield
keywords: Computational social science; Social media; Social network analysis; Suicide; Twitter