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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Vydáno v:	User modeling and user-adapted interaction Ročník 26; číslo 2-3; s. 109 - 142
Hlavní autoři:	Farnadi, Golnoosh, Sitaraman, Geetha, Sushmita, Shanu, Celli, Fabio, Kosinski, Michal, Stillwell, David, Davalos, Sergio, Moens, Marie-Francine, De Cock, Martine
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Dordrecht Springer Netherlands 01.06.2016 Springer Nature B.V
Témata:	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
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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? and (3) What is the decay in accuracy when porting models trained in one social media environment to another?
AbstractList	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?
Author	Sitaraman, Geetha Davalos, Sergio Farnadi, Golnoosh Moens, Marie-Francine Sushmita, Shanu Kosinski, Michal Celli, Fabio De Cock, Martine Stillwell, David
Author_xml	– sequence: 1 givenname: Golnoosh surname: Farnadi fullname: Farnadi, Golnoosh email: Golnoosh.farnadi@ugent.be organization: Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Department of Computer Science, Katholieke Universiteit Leuven – sequence: 2 givenname: Geetha surname: Sitaraman fullname: Sitaraman, Geetha organization: Center for Data Science, University of Washington Tacoma – sequence: 3 givenname: Shanu surname: Sushmita fullname: Sushmita, Shanu organization: Center for Data Science, University of Washington Tacoma – sequence: 4 givenname: Fabio surname: Celli fullname: Celli, Fabio organization: Center for Mind/Brain Sciences, University of Trento – sequence: 5 givenname: Michal surname: Kosinski fullname: Kosinski, Michal organization: Stanford Graduate School of Business, Stanford University – sequence: 6 givenname: David surname: Stillwell fullname: Stillwell, David organization: Judge Business School, University of Cambridge – sequence: 7 givenname: Sergio surname: Davalos fullname: Davalos, Sergio organization: Milgard School of Business, University of Washington Tacoma – sequence: 8 givenname: Marie-Francine surname: Moens fullname: Moens, Marie-Francine organization: Department of Computer Science, Katholieke Universiteit Leuven – sequence: 9 givenname: Martine surname: De Cock fullname: De Cock, Martine organization: Center for Data Science, University of Washington Tacoma
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Snippet	A variety of approaches have been recently proposed to automatically infer users’ personality from their user generated content in social media. Approaches... A variety of approaches have been recently proposed to automatically infer users' personality from their user generated content in social media. Approaches...
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SubjectTerms	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
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