Personality computing

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Personality computing is a research field related to artificial intelligence and personality psychology that studies personality by means of computational techniques from different sources, including text, multimedia and social networks.

Overview[edit]

Personality computing addresses three main problems involving personality: automatic personality recognition, perception and synthesis.[1]. Automatic personality recognition is the inference of personality type of target individuals from their digital footprint, automatic personality perception is the inference of the personality attributed by an observer to a target individual based on some observable behavior, and automatic personality synthesis is the generation of the style or behaviour of artificial personalities in Avatars and virtual agents.

Self-assessed personality tests or observer ratings are always exploited as the ground truth for testing and validating the performance of artificial intelligence algorithms for the automatic prediction of personality types[2]. There is a wide variety of personality tests, such as the Myers Briggs Type Indicator (MBTI)[3] or the MMPI, but the most used are tests based on the Five Factor Model such as the Revised NEO Personality Inventory [4].

History[edit]

Personality computing begun around 2005 with few pioneering research works in personality recognition showing that personality traits could be inferred with reasonable accuracy from text, such as blogs, self-presentations[5][6][7], and email addresses[8].

Few years later begun the research in personality recognition and perception from multimodal and social signals, such as recorded meetings[9] and voice calls[10].

In the 2010s the research focussed mainly on personality recognition and perception from social media, in particular from Facebook[11][12][13], Twitter[14] and Instagram[15]. In the same years Automatic personality synthesis helped improving the coherence of simulated behavior in virtual agents[16].

Scientific works demonstrated the validity of Personality Computing from different digital footprints, in particular from user preferences such as Facebook page likes[17] and showed that machines can recognize personality better than humans[18]

Applications[edit]

Personality computing techniques, in particular personality recongition and perception, have applications in Social media marketing, where they can help reducing the cost of advertising campaigns through psychological targeting[19] [20]

References[edit]

  1. ^ [1]Vinciarelli, Alessandro, and Gelareh Mohammadi. "A survey of personality computing." IEEE Transactions on Affective Computing 5.3 (2014): 273-291.
  2. ^ Celli, Fabio, et al. "Workshop on computational personality recognition (shared task)." Proceedings of the Workshop on Computational Personality Recognition. 2013.
  3. ^ Isabel Briggs Myers and Peter B Myers. 2010. Giftsdiffering: Understanding personality type. Davies-Black Publishing.
  4. ^ Paul T Costa and Robert R McCrae. 2008. The re-vised neo personality inventory (neo-pi-r).In G.J.Boyle, G Matthews and D. Saklofske (Eds.). TheSAGE handbook of personality theory and assessment2:179–198
  5. ^ Argamon, Shlomo, et al. "Lexical predictors of personality type." (2005).
  6. ^ Oberlander, Jon, and Scott Nowson. "Whose thumb is it anyway?: classifying author personality from weblog text." Proceedings of the COLING/ACL on Main conference poster sessions. Association for Computational Linguistics, 2006.
  7. ^ Mairesse, François, et al. "Using linguistic cues for the automatic recognition of personality in conversation and text." Journal of artificial intelligence research 30 (2007): 457-500.
  8. ^ Back, Mitja D., Stefan C. Schmukle, and Boris Egloff. "How extraverted is honey. bunny77@ hotmail. de? Inferring personality from e-mail addresses." Journal of Research in Personality 42.4 (2008): 1116-1122.
  9. ^ Pianesi, Fabio, et al. "Multimodal recognition of personality traits in social interactions." Proceedings of the 10th international conference on Multimodal interfaces. ACM, 2008.
  10. ^ Mohammadi, Gelareh, and Alessandro Vinciarelli. "Automatic personality perception: Prediction of trait attribution based on prosodic features." IEEE Transactions on Affective Computing 3.3 (2012): 273-284.
  11. ^ Quercia, Daniele, et al. "The personality of popular Facebook users." Proceedings of the ACM 2012 conference on computer supported cooperative work. ACM, 2012.
  12. ^ Schwartz, H. Andrew, et al. "Personality, gender, and age in the language of social media: The open-vocabulary approach." PloS one 8.9 (2013): e73791.
  13. ^ [2]Celli, Fabio, Elia Bruni, and Bruno Lepri. "Automatic personality and interaction style recognition from Facebook profile pictures." Proceedings of the 22nd ACM international conference on Multimedia. ACM, 2014.
  14. ^ Golbeck, Jennifer, et al. "Predicting personality from twitter." Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on. IEEE, 2011.
  15. ^ Ferwerda, Bruce, Markus Schedl, and Marko Tkalcic. "Predicting personality traits with instagram pictures." Proceedings of the 3rd Workshop on Emotions and Personality in Personalized Systems 2015. ACM, 2015.
  16. ^ Faur, Caroline, et al. "PERSEED: a self-based model of personality for virtual agents inspired by socio-cognitive theories." Affective Computing and Intelligent Interaction (ACII), 2013 Humaine Association Conference on. IEEE, 2013.
  17. ^ Kosinski, Michal, David Stillwell, and Thore Graepel. "Private traits and attributes are predictable from digital records of human behavior." Proceedings of the National Academy of Sciences (2013): 201218772.
  18. ^ Youyou, Wu, Michal Kosinski, and David Stillwell. "Computer-based personality judgments are more accurate than those made by humans." Proceedings of the National Academy of Sciences 112.4 (2015): 1036-1040.
  19. ^ Matz, S. C., et al. "Psychological targeting as an effective approach to digital mass persuasion." Proceedings of the National Academy of Sciences (2017): 201710966.
  20. ^ Celli, Fabio, Pietro Zani Massani, and Bruno Lepri. "Profilio: Psychometric Profiling to Boost Social Media Advertising." Proceedings of the 2017 ACM on Multimedia Conference. ACM, 2017. [3]