Personality detection by analysis of Twitter profiles

Mehul Smriti Raje, Aakarsh Singh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the boom of social networks, the amount of personal information that is put online has seen an exponential increase. Social media profiles can reveal important details about individuals and their preferences. Psychological studies have revealed that humans can be divided into types, based on their personality traits. This paper explores the usefulness of Twitter profiles in predicting the personality types of the users. The results of analysis of 450 Twitter profiles and over 1 million tweets are reported. It is shown that high degrees of correlation exist between the information from tweets and personality traits. Prediction accuracies up to 60% are also reported.

Original languageEnglish
Title of host publicationProceedings of the 8th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2016
PublisherSpringer Verlag
Pages667-675
Number of pages9
Volume614
ISBN (Print)9783319606170
DOIs
Publication statusPublished - 2018
Externally publishedYes
Event8th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2016 - Vellore, India
Duration: 19-12-201621-12-2016

Publication series

NameAdvances in Intelligent Systems and Computing
Volume614
ISSN (Print)2194-5357

Conference

Conference8th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2016
CountryIndia
CityVellore
Period19-12-1621-12-16

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Raje, M. S., & Singh, A. (2018). Personality detection by analysis of Twitter profiles. In Proceedings of the 8th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2016 (Vol. 614, pp. 667-675). (Advances in Intelligent Systems and Computing; Vol. 614). Springer Verlag. https://doi.org/10.1007/978-3-319-60618-7_65
Raje, Mehul Smriti ; Singh, Aakarsh. / Personality detection by analysis of Twitter profiles. Proceedings of the 8th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2016. Vol. 614 Springer Verlag, 2018. pp. 667-675 (Advances in Intelligent Systems and Computing).
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Raje, MS & Singh, A 2018, Personality detection by analysis of Twitter profiles. in Proceedings of the 8th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2016. vol. 614, Advances in Intelligent Systems and Computing, vol. 614, Springer Verlag, pp. 667-675, 8th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2016, Vellore, India, 19-12-16. https://doi.org/10.1007/978-3-319-60618-7_65

Personality detection by analysis of Twitter profiles. / Raje, Mehul Smriti; Singh, Aakarsh.

Proceedings of the 8th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2016. Vol. 614 Springer Verlag, 2018. p. 667-675 (Advances in Intelligent Systems and Computing; Vol. 614).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - With the boom of social networks, the amount of personal information that is put online has seen an exponential increase. Social media profiles can reveal important details about individuals and their preferences. Psychological studies have revealed that humans can be divided into types, based on their personality traits. This paper explores the usefulness of Twitter profiles in predicting the personality types of the users. The results of analysis of 450 Twitter profiles and over 1 million tweets are reported. It is shown that high degrees of correlation exist between the information from tweets and personality traits. Prediction accuracies up to 60% are also reported.

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Raje MS, Singh A. Personality detection by analysis of Twitter profiles. In Proceedings of the 8th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2016. Vol. 614. Springer Verlag. 2018. p. 667-675. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-319-60618-7_65