%0 Conference Paper %1 quercia2011twitter %A Quercia, D. %A Kosinski, M. %A Stillwell, D. %A Crowcroft, J. %B Proceedings of the Third International Conference on Social Computing (SocialCom) and the Third International Conference on Privacy, Security, Risk and Trust (PASSAT) %D 2011 %I IEEE %K classification personality prediction profile twitter user %P 180--185 %R 10.1109/PASSAT/SocialCom.2011.26 %T Our Twitter Profiles, Our Selves: Predicting Personality with Twitter %U http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6113111&tag=1 %X 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 0.88 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 media.

@inproceedings{quercia2011twitter, 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 0.88 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 media.}, added-at = {2015-01-19T11:14:58.000+0100}, author = {Quercia, D. and Kosinski, M. and Stillwell, D. and Crowcroft, J.}, biburl = {https://www.bibsonomy.org/bibtex/2be803d6ce6ec80e96c3b92e0a969aa75/jaeschke}, booktitle = {Proceedings of the Third International Conference on Social Computing (SocialCom) and the Third International Conference on Privacy, Security, Risk and Trust (PASSAT)}, doi = {10.1109/PASSAT/SocialCom.2011.26}, interhash = {5f80d13e62baf599ce254628d5a7e014}, intrahash = {be803d6ce6ec80e96c3b92e0a969aa75}, keywords = {classification personality prediction profile twitter user}, month = oct, pages = {180--185}, publisher = {IEEE}, timestamp = {2015-01-19T11:14:58.000+0100}, title = {Our Twitter Profiles, Our Selves: Predicting Personality with Twitter}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6113111&tag=1}, year = 2011 }