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...

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Bibliographic Details
Published in:User modeling and user-adapted interaction Vol. 26; no. 2-3; pp. 109 - 142
Main Authors: Farnadi, Golnoosh, Sitaraman, Geetha, Sushmita, Shanu, Celli, Fabio, Kosinski, Michal, Stillwell, David, Davalos, Sergio, Moens, Marie-Francine, De Cock, Martine
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.06.2016
Springer Nature B.V
Subjects:
Accuracy
Comparative analysis
Computation
Computer Science
Datasets
Digital media
Emotions
Management of Computing and Information Systems
Mathematical models
Multimedia Information Systems
On-line systems
Personality
Personality traits
Psychology
Questionnaires
Recognition
Social networks
Studies
Tasks
User generated content
User Interfaces and Human Computer Interaction
User generated content
Multivariate regression
Feature analysis
Big Five personality
Social media
ISSN:0924-1868, 1573-1391
Online Access:Get full text
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Summary: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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ISSN:0924-1868
1573-1391
DOI:10.1007/s11257-016-9171-0