Dark personalities on Facebook: Harmful online behaviors and language

Abstract The goal of this paper was to assess the connection between dark personality traits and engagement in harmful online behaviors in a sample of Russian Facebook users, and to describe the language they use in online communication. A total of 6724 individuals participated in the study (mean age = 44.96 years, age range: 18–85 years, 77.9% — female). Data was collected via a purpose-built application, which served two purposes: administer the survey and download consenting user's public wall posts, gender and age from the Facebook profile. The survey included questions on engagement in harmful online behaviors and the Short Dark Triad scale; 15,281 wall posts from 1972 users were included in the dataset. These posts were subjected to morphological, lexical and semantic analyses. More than 25% of the sample reported engaging in harmful online behaviors. Males were more likely to send insulting or threatening messages and post aggressive comments; no gender differences were found for disseminating other people's private information. Psychopathy and male gender were the unique predictors of engagement in harmful online behaviors. A number of significant correlations were found between the dark traits and numeric, lexical, morphological and semantic characteristics of the participants' posts.

[1]  Jeffrey T. Hancock,et al.  Hungry like the wolf: A word‐pattern analysis of the language of psychopaths , 2013 .

[2]  Cindy K. Chung,et al.  The Relations Between Personality and Language Use , 2007, The Journal of general psychology.

[3]  Ari Pirkola,et al.  Morphological typology of languages for IR , 2001, J. Documentation.

[4]  Mikhail Korobov,et al.  Morphological Analyzer and Generator for Russian and Ukrainian Languages , 2015, AIST.

[5]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[6]  Alan K. Goodboy,et al.  The personality profile of a cyberbully: Examining the Dark Triad , 2015, Comput. Hum. Behav..

[7]  Evita March,et al.  The dark side of Facebook®: The Dark Tetrad, negative social potency, and trolling behaviours , 2016 .

[8]  T Litvinova,et al.  CORPUS STUDIES OF SPEECH OF INDIVIDUALS WHO COMMITTED SUICIDES , 2016 .

[9]  Marco Baroni,et al.  Frege in Space: A Program of Compositional Distributional Semantics , 2014 .

[10]  Andrew McCallum,et al.  Distributional clustering of words for text classification , 1998, SIGIR '98.

[11]  J. Greenberg A Quantitative Approach to the Morphological Typology of Language , 1960, International Journal of American Linguistics.

[12]  Angela Baldasare,et al.  Cyber Aggression Among College Students: Demographic Differences, Predictors of Distress, and the Role of the University , 2015 .

[13]  R. Grieve,et al.  Trolling on Tinder® (and other dating apps): Examining the role of the Dark Tetrad and impulsivity , 2017 .

[14]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[15]  Allison M. Schenk,et al.  Characteristics of college cyberbullies , 2013, Comput. Hum. Behav..

[16]  C. J. van Rijsbergen,et al.  Geometry and Meaning , 2006, Comput. Linguistics.

[17]  Megan A. Moreno,et al.  Cyberbullying, Depression, and Problem Alcohol Use in Female College Students: A Multisite Study , 2015, Cyberpsychology Behav. Soc. Netw..

[18]  Jovana Bjekić,et al.  Psychometric evaluation of the Serbian dictionary for automatic text analysis - LIWCser , 2014 .

[19]  D. Sculley,et al.  Web-scale k-means clustering , 2010, WWW '10.

[20]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[21]  D. Paulhus,et al.  The Dark Triad of personality: Narcissism, Machiavellianism, and psychopathy , 2002 .

[22]  Evita March,et al.  Predicting perpetration of intimate partner cyberstalking: Gender and the Dark Tetrad , 2017, Comput. Hum. Behav..

[23]  Fenne große Deters,et al.  Narcissism and the Use of Personal Pronouns Revisited , 2022 .

[24]  Polina Panicheva,et al.  Lexical, morphological and semantic correlates of the dark triad personality traits in russian facebook texts , 2016, 2016 IEEE Artificial Intelligence and Natural Language Conference (AINL).

[25]  Dominic Widdows,et al.  Geometry and Meaning , 2004, Computational Linguistics.

[26]  Andrey Kutuzov,et al.  Texts in, meaning out: neural language models in semantic similarity task for Russian , 2015, ArXiv.

[27]  Andrey Kutuzov,et al.  WebVectors: A Toolkit for Building Web Interfaces for Vector Semantic Models , 2016, AIST.

[28]  Steven Cummins,et al.  Longitudinal Associations Between Cyberbullying Involvement and Adolescent Mental Health. , 2016, The Journal of adolescent health : official publication of the Society for Adolescent Medicine.

[29]  Margaret L. Kern,et al.  Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach , 2013, PloS one.

[30]  Benedikt Szmrecsanyi,et al.  Measuring analyticity and syntheticity in creoles , 2014 .

[31]  Christian Biemann,et al.  Chinese Whispers - an Efficient Graph Clustering Algorithm and its Application to Natural Language Processing Problems , 2006 .

[32]  Mark D. Griffiths,et al.  An Exploratory Study of Trolling in Online Video Gaming , 2012, Int. J. Cyber Behav. Psychol. Learn..

[33]  Heidi Vandebosch,et al.  Dark Triad personality traits and adolescent cyber-aggression , 2015 .

[34]  Robert Slonje,et al.  The nature of cyberbullying, and strategies for prevention , 2013, Comput. Hum. Behav..

[35]  D. Paulhus,et al.  Trolls just want to have fun , 2014 .

[36]  J. Pennebaker,et al.  The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods , 2010 .

[37]  A. Joinson,et al.  Characterizing the Linguistic Chameleon: Personal and Social Correlates of Linguistic Style Accommodation , 2016 .

[38]  Paul Vedder,et al.  Which personality traits are related to traditional bullying and cyberbullying? A study with the Big Five, Dark Triad and sadism , 2017 .

[39]  M. Geel,et al.  Relationship between peer victimization, cyberbullying, and suicide in children and adolescents , 2014 .

[40]  Daniel N. Jones,et al.  Introducing the Short Dark Triad (SD3) , 2014, Assessment.

[41]  Ewout H. Meijer,et al.  The Malevolent Side of Human Nature , 2017, Perspectives on psychological science : a journal of the Association for Psychological Science.

[42]  Polina Panicheva,et al.  Violence Exposure, Posttraumatic Stress, and Subjective Well-Being in a Sample of Russian Adults: A Facebook-Based Study , 2017, Journal of interpersonal violence.

[43]  Delroy L. Paulhus,et al.  Toward a Taxonomy of Dark Personalities , 2014 .

[44]  Gregory J. Park,et al.  Predicting Dark Triad Personality Traits from Twitter Usage and a Linguistic Analysis of Tweets , 2012, 2012 11th International Conference on Machine Learning and Applications.

[45]  Manuel Gámez-Guadix,et al.  Cyberbullying Victimization and Depression in Adolescents: The Mediating Role of Body Image and Cognitive Schemas in a One-year Prospective Study , 2015, European Journal on Criminal Policy and Research.

[46]  Manuel Gámez-Guadix,et al.  Longitudinal and reciprocal relations of cyberbullying with depression, substance use, and problematic internet use among adolescents. , 2013, The Journal of adolescent health : official publication of the Society for Adolescent Medicine.

[47]  Paul Vedder,et al.  Relationship between peer victimization, cyberbullying, and suicide in children and adolescents: a meta-analysis. , 2014, JAMA pediatrics.

[48]  Mark Dredze,et al.  Quantifying Mental Health Signals in Twitter , 2014, CLPsych@ACL.

[49]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[50]  J. Pennebaker,et al.  Psychological aspects of natural language. use: our words, our selves. , 2003, Annual review of psychology.