Analysing the connectivity and communication of suicidal users on twitter

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.

[1]  M. Williams,et al.  Who Tweets? Deriving the Demographic Characteristics of Age, Occupation and Social Class from Twitter User Meta-Data , 2015, PloS one.

[2]  Pascal Poncelet,et al.  Mining Twitter for Suicide Prevention , 2014, NLDB.

[3]  Gil-Young Song,et al.  Predicting National Suicide Numbers with Social Media Data , 2013, PloS one.

[4]  M. Gould.  Suicide and the Media , 2001, Annals of the New York Academy of Sciences.

[5]  Vaibhavi N Patodkar,et al.  Twitter as a Corpus for Sentiment Analysis and Opinion Mining , 2016 .

[6]  Keith Hawton,et al.  Suicide clusters: a review of risk factors and mechanisms. , 2013, Suicide & life-threatening behavior.

[7]  Lindsay H. Shaw,et al.  In Defense of the Internet: The Relationship between Internet Communication and Depression, Loneliness, Self-Esteem, and Perceived Social Support , 2002, Cyberpsychology Behav. Soc. Netw..

[8]  P. Burnap,et al.  A Naïve Bayes Approach to Classifying Topics in Suicide Notes , 2012, Biomedical informatics insights.

[9]  L. Flashman,et al.  Predicting the Risk of Suicide by Analyzing the Text of Clinical Notes , 2014, PloS one.

[10]  Marco Conti,et al.  Dynamics of personal social relationships in online social networks: a study on twitter , 2013, COSN '13.

[11]  Jimmy J. Lin,et al.  Information network or social network?: the structure of the twitter follow graph , 2014, WWW.

[12]  Robert C. Hsiung,et al.  A Suicide in an Online Mental Health Support Group: Reactions of the Group Members, Administrative Responses, and Recommendations , 2007, Cyberpsychology Behav. Soc. Netw..

[13]  Munmun De Choudhury,et al.  Detecting and Characterizing Mental Health Related Self-Disclosure in Social Media , 2015, CHI Extended Abstracts.

[14]  J. Pirkis,et al.  Suicide and the media. Part II: Portrayal in fictional media. , 2001, Crisis.

[15]  Fernando Martín-Sánchez,et al.  Health outcomes and related effects of using social media in chronic disease management: A literature review and analysis of affordances , 2013, J. Biomed. Informatics.

[16]  Mike Thelwall,et al.  Sentiment in short strength detection informal text , 2010 .

[17]  A. Öjehagen,et al.  The social network of people who attempt suicide , 1992, Acta psychiatrica Scandinavica.

[18]  Christos Faloutsos,et al.  Graphs over time: densification laws, shrinking diameters and possible explanations , 2005, KDD '05.

[19]  Junlan Feng,et al.  Robust Sentiment Detection on Twitter from Biased and Noisy Data , 2010, COLING.

[20]  A. Leenaars,et al.  Suicide Note Classification Using Natural Language Processing: A Content Analysis , 2010, Biomedical informatics insights.

[21]  Adam Michael Edwards,et al.  Detecting tension in online communities with computational Twitter analysis , 2015 .

[22]  Michael D. Barnes,et al.  Tracking suicide risk factors through Twitter in the US. , 2014, Crisis.

[23]  G A Colditz,et al.  A prospective study of social networks in relation to total mortality and cardiovascular disease in men in the USA. , 1996, Journal of epidemiology and community health.

[24]  Will Webberley,et al.  Retweeting: A study of message-forwarding in twitter , 2011, 2011 Workshop on Mobile and Online Social Networks.

[25]  Redmond Wa,et al.  Characterizing and Predicting Postpartum Depression from Shared Facebook Data Munmun De Choudhury Scott Counts Eric Horvitz Aaron Hoff , 2014 .

[26]  Robert Cole,et al.  Computer Communications , 1982, Springer New York.

[27]  P. Bearman,et al.  Suicide and friendships among American adolescents. , 2004, American journal of public health.

[28]  Kristina Lerman,et al.  Information Contagion: An Empirical Study of the Spread of News on Digg and Twitter Social Networks , 2010, ICWSM.

[29]  Daniele Quercia,et al.  The Social World of Twitter: Topics, Geography, and Emotions , 2012, ICWSM.

[30]  Jonathan Scourfield,et al.  Suicide prevention via the Internet: a descriptive review. , 2014, Crisis.

[31]  Pete Burnap,et al.  Machine Classification and Analysis of Suicide-Related Communication on Twitter , 2015, HT.

[32]  K. Hawton,et al.  The Power of the Web: A Systematic Review of Studies of the Influence of the Internet on Self-Harm and Suicide in Young People , 2013, PloS one.

[33]  Eric Horvitz,et al.  Characterizing and predicting postpartum depression from shared facebook data , 2014, CSCW.

[34]  Yue Liu,et al.  Aggregate Characterization of User Behavior in Twitter and Analysis of the Retweet Graph , 2014, ACM Trans. Internet Techn..

[35]  Mark S. Granovetter.  The Strength of Weak Ties , 1973, American Journal of Sociology.

[36]  Daniel Romer,et al.  Media Contagion and Suicide Among the Young , 2003 .

[37]  Elizabeth D. Cox,et al.  Feeling bad on Facebook: depression disclosures by college students on a social networking site , 2011, Depression and anxiety.

[38]  David A. Bader,et al.  Massive Social Network Analysis: Mining Twitter for Social Good , 2010, 2010 39th International Conference on Parallel Processing.

[39]  Jérôme Kunegis,et al.  KONECT: the Koblenz network collection , 2013, WWW.