Proceedings ArticleDOI
Our Twitter Profiles, Our Selves: Predicting Personality with Twitter
Daniele Quercia,Michal Kosinski,David Stillwell,Jon Crowcroft +3 more
- pp 180-185
TLDR
It is argued that being able to predict user personality goes well beyond the initial goal of informing the design of new personalized applications as it, for example, expands current studies on privacy in social media.Abstract:
Psychological personality has been shown to affect a variety of aspects: preferences for interaction styles in the digital world and for music genres, for example Consequently, the design of personalized user interfaces and music recommender systems might benefit from understanding the relationship between personality and use of social media Since there has not been a study between personality and use of Twitter at large, we set out to analyze the relationship between personality and different types of Twitter users, including popular users and influentials For 335 users, we gather personality data, analyze it, and find that both popular users and influentials are extroverts and emotionally stable (low in the trait of Neuroticism) Interestingly, we also find that popular users are `imaginative' (high in Openness), while influentials tend to be `organized' (high in Conscientiousness) We then show a way of accurately predicting a user's personality simply based on three counts publicly available on profiles: following, followers, and listed counts Knowing these three quantities about an active user, one can predict the user's five personality traits with a root-mean-squared error below 088 on a $[1,5]$ scale Based on these promising results, we argue that being able to predict user personality goes well beyond our initial goal of informing the design of new personalized applications as it, for example, expands current studies on privacy in social mediaread more
Citations
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To illuminate and motivate: a fuzzy-trace model of the spread of information online
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In data we trust? The implications of datafication for social monitoring
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Inferring Students’ Personality from Their Communication Behavior in Web-based Learning Systems
TL;DR: This paper identifies a set of features that are significantly influenced by students’ personality, which not only include their communication activities carried out in both synchronous and asynchronous web-based learning environment, but also their linguistic content in conversational texts, and develops inference model to unify these features for determiningStudents’ five personality traits.
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Profilio: Psychometric Profiling to Boost Social Media Advertising
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