Personality computing

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

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

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] In 2008, the concept of "portable personality" for the distributed management of personality profiles has been developed.[9]

Few years later begun the research in personality recognition and perception from multimodal and social signals, such as recorded meetings[10] and voice calls.[11]

In the 2010s the research focussed mainly on personality recognition and perception from social media, in particular from Facebook,[12][13][14] Twitter[15] and Instagram.[16] In the same years Automatic personality synthesis helped improving the coherence of simulated behavior in virtual agents.[17]

Scientific works demonstrated the validity of Personality Computing from different digital footprints, in particular from user preferences such as Facebook page likes[18] and showed that machines can recognize personality better than humans[19]

Applications

Personality computing techniques, in particular personality recognition and perception, have applications in Social media marketing, where they can help reducing the cost of advertising campaigns through psychological targeting[20][21]

References
  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. Lugmayr, Artur; Reymann, Simon; Kemper, Stefan; Dorsch, Tillmann; Roman, Pablo (December 2008). "Bits of Personality Everywhere: Implicit User-Generated Content in the Age of Ambient Media". 2008 IEEE International Symposium on Parallel and Distributed Processing with Applications: 516–521. doi:10.1109/ISPA.2008.141. ISBN 978-0-7695-3471-8.
  10. Pianesi, Fabio, et al. "Multimodal recognition of personality traits in social interactions." Proceedings of the 10th international conference on Multimodal interfaces. ACM, 2008.
  11. 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.
  12. Quercia, Daniele, et al. "The personality of popular Facebook users." Proceedings of the ACM 2012 conference on computer supported cooperative work. ACM, 2012.
  13. 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.
  14. 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.
  15. Golbeck, Jennifer, et al. "Predicting personality from twitter." Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third International Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on. IEEE, 2011.
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. Matz, S. C., et al. "Psychological targeting as an effective approach to digital mass persuasion." Proceedings of the National Academy of Sciences (2017): 201710966.
  21. 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.
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