Fabio Celli, post-doctoral researcher at University of Trento, Trento, Italy

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Abstract: Personality Recognition consists in the automatic prediction of people's personality traits from various sources, including text and multimedia, such as pictures, audio and video. These predictions can be exploited for a wide variety of tasks, research fields and real world applications, like recommender systems, user profiling, marketing, HR and security. Personality recognition has grown considerably in the last decade as a research field, and has been boosted in recent years by the availability of large databases of self-assessed data. Gold standard labels for an objective evaluation of personality types can be obtained by means of the Big5 personality tests, which are well-known and widely accepted in psychology and other research fields. The Big5 factor model defines personality along 5 bipolar scales: extraversion (sociable vs. shy); emotional stability (secure vs. neurotic); agreeableness (friendly vs. ugly); conscientiousness (organized vs. careless) and openness to experience (insightful vs. unimaginative).

From a computational perspective there are three main approaches to the recognition of personality from text: top-down, bottom-up and mixed, and each one has advantages and disadvantages when applied to specific tasks. In this talk we will present an overview of the three approaches to the computational recognition of personality, we will see the results of evaluation campaigns and applications in social media and security, in order to provide insights on what works and what doesn't, under which conditions. Bibliography:

  • [1] Celli, Fabio and Pianesi, Fabio and Stillwell, David and Kosinski, Michal, (2013). Workshop on Computational Personality Recognition: Shared Task, Proceedings of ICWSM 2013. pp. 345-348
  • [2] Celli, Fabio and Rossi, Luca (2012). The role of emotional stability in Twitter conversations, Proceedings of the Workshop on Semantic Analysis in Social Media, Association for Computational Linguistics, pp 10-17
  • [3] Celli, Fabio and Lepri, Bruno and Biel, Joan-Isaac and Gatica-Perez, Daniel and Riccardi, Giuseppe and Pianesi, Fabio (2014). Workshop on Computational Personality Recognition 2014, Proceedings of the ACM International Conference on Multimedia, pp. 1245-1247
  • [4] Vinciarelli Alessandro and Mohammadi, Gelareh (2014). A Survey of Personality Computing, In IEEE Transactions on Affective Computing, 3(5), pp 1-13