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PARIS project is part of the SBO Program of the IWT (IWT-SBO-Nr. 110067)
Abstract
A variety of approaches have been recently proposed to automatically infer users' personality from their user generated content in social media. Approaches differ in terms of the machine learning algorithms and the feature sets used, type of utilized footprint, and the social media environment used to collect the data. In this paper, we perform a comparative analysis of state-of-the-art computational personality recognition methods on a varied set of social media ground truth data from Facebook, Twitter and YouTube. We answer three questions: (1) Should personality prediction be treated as a multi-label prediction task (i.e., all personality traits of a given user are predicted at once), or should each trait be identified separately? (2) Which predictive features work well across different on-line environments? (3) What is the decay in accuracy when porting models trained in one social media environment to another?
Keywords
Social media, Feature analysis, Big Five personality, Multivariate regression, User generated content, TEXT, FACEBOOK

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Citation

Please use this url to cite or link to this publication:

MLA
Farnadi, Golnoosh et al. “Computational Personality Recognition in Social Media.” USER MODELING AND USER-ADAPTED INTERACTION 26.2-3 (2016): 109–142. Print.
APA
Farnadi, G., Sitaraman, G., Sushmita, S., Celli, F., Kosinski, M., Stillwell, D., Davalos, S., et al. (2016). Computational personality recognition in social media. USER MODELING AND USER-ADAPTED INTERACTION, 26(2-3), 109–142.
Chicago author-date
Farnadi, Golnoosh, Geetha Sitaraman, Shanu Sushmita, Fabio Celli, Michal Kosinski, David Stillwell, Sergio Davalos, Marie-Francine Moens, and Martine De Cock. 2016. “Computational Personality Recognition in Social Media.” User Modeling and User-adapted Interaction 26 (2-3): 109–142.
Chicago author-date (all authors)
Farnadi, Golnoosh, Geetha Sitaraman, Shanu Sushmita, Fabio Celli, Michal Kosinski, David Stillwell, Sergio Davalos, Marie-Francine Moens, and Martine De Cock. 2016. “Computational Personality Recognition in Social Media.” User Modeling and User-adapted Interaction 26 (2-3): 109–142.
Vancouver
1.
Farnadi G, Sitaraman G, Sushmita S, Celli F, Kosinski M, Stillwell D, et al. Computational personality recognition in social media. USER MODELING AND USER-ADAPTED INTERACTION. 2016;26(2-3):109–42.
IEEE
[1]
G. Farnadi et al., “Computational personality recognition in social media,” USER MODELING AND USER-ADAPTED INTERACTION, vol. 26, no. 2–3, pp. 109–142, 2016.
@article{7100092,
  abstract     = {A variety of approaches have been recently proposed to automatically infer users' personality from their user generated content in social media. Approaches differ in terms of the machine learning algorithms and the feature sets used, type of utilized footprint, and the social media environment used to collect the data. In this paper, we perform a comparative analysis of state-of-the-art computational personality recognition methods on a varied set of social media ground truth data from Facebook, Twitter and YouTube. We answer three questions: (1) Should personality prediction be treated as a multi-label prediction task (i.e., all personality traits of a given user are predicted at once), or should each trait be identified separately? (2) Which predictive features work well across different on-line environments? (3) What is the decay in accuracy when porting models trained in one social media environment to another?},
  author       = {Farnadi, Golnoosh and Sitaraman, Geetha and Sushmita, Shanu and Celli, Fabio and Kosinski, Michal and Stillwell, David and Davalos, Sergio and Moens, Marie-Francine and De Cock, Martine},
  issn         = {0924-1868},
  journal      = {USER MODELING AND USER-ADAPTED INTERACTION},
  keywords     = {Social media,Feature analysis,Big Five personality,Multivariate regression,User generated content,TEXT,FACEBOOK},
  language     = {eng},
  number       = {2-3},
  pages        = {109--142},
  title        = {Computational personality recognition in social media},
  url          = {http://dx.doi.org/10.1007/s11257-016-9171-0},
  volume       = {26},
  year         = {2016},
}

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