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Computational personality recognition in social media


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? and (3) What is the decay in accuracy when porting models trained in one social media environment to another?

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    We compute the correlation among all features and personality traits and find the significant correlated features. The full list of features and their correlation scores can be downloaded from the supplementary materials of this manuscript.


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We would like to thank the anonymous reviewers for their helpful comments and suggestions. This work was funded in part by the SBO-program of the Flemish Agency for Innovation by Science and Technology (IWT-SBO-Nr. 110067).

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Correspondence to Golnoosh Farnadi.

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Farnadi, G., Sitaraman, G., Sushmita, S. et al. Computational personality recognition in social media. User Model User-Adap Inter 26, 109–142 (2016).

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  • Big Five personality
  • Social media
  • User generated content
  • Multivariate regression
  • Feature analysis