Psychopaths Online: Modeling Psychopathy in Social Media Discourse

This is an exploratory study examining the relationship between discourse patterns in social media and undergraduate students’ levels of psychopathy when compared to discourse patterns in narratives produced in a laboratory. It expands on previous research findings that psychopathic murderers exhibit narcissistic tendencies and psychological distancing in their discourse when compared to non-psychopathic murderers. Undergraduate students’ emails, SMS messages, and Facebook messages were collected and analyzed in relation to their scores on the Self-Report Psychopathy Test III (SRP III). Findings support both main hypotheses: that discourse patterns in social media are distinctly different from discourse patterns in narratives produced in a laboratory, and that psychopathic tendencies are identifiable in social media discourse. Consistent with previous studies, students higher in psychopathy showed evidence of psychological distancing, produced less comprehensible language, potentially reflecting their low reading achievement levels, and produced more anger and swear words, consistent with the emotional deficits and disagreeableness central to psychopathy

Psychopaths Online: The Linguistic Traces of Psychopathy in Email, Text Messaging and Facebook

Individuals high in psychopathy are interpersonally manipulative, exhibit callous affect, and have criminal tendencies. The present study examines whether these attributes of psychopathy are correlated with linguistic patterns present in everyday online communication. Participants’ emails, SMS messages, and Facebook messages were collected and analyzed in relation to their scores on the Self-Report Psychopathy Test III. The findings suggest that psychopathic tendencies leave a trace in online discourse, and that different forms of online media sometimes moderate the association between a linguistic dimension and psychopathy scores. Consistent with previous studies and the emotional and interpersonal deficits central to psychopathy, participants higher in psychopathy showed more evidence of psychological distancing, wrote less comprehensible discourse, and produced more interpersonally hostile language. The results reveal that linguistic traces of psychopathy can be detected in online communication, and that those with higher traits of psychopathy fail to modify their language use across media types

G.J.: Predicting dark triad personality traits from twitter usage and a linguistic analysis of tweets

Abstract-Social media sites are now the most popular destination for Internet users, providing social scientists with a great opportunity to understand online behaviour. There are a growing number of research papers related to social media, a small number of which focus on personality prediction. To date, studies have typically focused on the Big Five traits of personality, but one area which is relatively unexplored is that of the anti-social traits of narcissism, Machiavellianism and psychopathy, commonly referred to as the Dark Triad. This study explored the extent to which it is possible to determine antisocial personality traits based on Twitter use. This was performed by comparing the Dark Triad and Big Five personality traits of 2,927 Twitter users with their profile attributes and use of language. Analysis shows that there are some statistically significant relationships between these variables. Through the use of crowd sourced machine learning algorithms, we show that machine learning provides useful prediction rates, but is imperfect in predicting an individual's Dark Triad traits from Twitter activity. While predictive models may be unsuitable for predicting an individual's personality, they may still be of practical importance when models are applied to large groups of people, such as gaining the ability to see whether anti-social traits are increasing or decreasing over a population. Our results raise important questions related to the unregulated use of social media analysis for screening purposes. It is important that the practical and ethical implications of drawing conclusions about personal information embedded in social media sites are better understood

G.J.: Predicting dark triad personality traits from twitter usage and a linguistic analysis of tweets

Abstract-Social media sites are now the most popular destination for Internet users, providing social scientists with a great opportunity to understand online behaviour. There are a growing number of research papers related to social media, a small number of which focus on personality prediction. To date, studies have typically focused on the Big Five traits of personality, but one area which is relatively unexplored is that of the anti-social traits of narcissism, Machiavellianism and psychopathy, commonly referred to as the Dark Triad. This study explored the extent to which it is possible to determine antisocial personality traits based on Twitter use. This was performed by comparing the Dark Triad and Big Five personality traits of 2,927 Twitter users with their profile attributes and use of language. Analysis shows that there are some statistically significant relationships between these variables. Through the use of crowd sourced machine learning algorithms, we show that machine learning provides useful prediction rates, but is imperfect in predicting an individual's Dark Triad traits from Twitter activity. While predictive models may be unsuitable for predicting an individual's personality, they may still be of practical importance when models are applied to large groups of people, such as gaining the ability to see whether anti-social traits are increasing or decreasing over a population. Our results raise important questions related to the unregulated use of social media analysis for screening purposes. It is important that the practical and ethical implications of drawing conclusions about personal information embedded in social media sites are better understood