The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Predicting Personality from Twitter
Abstract
Social media is a place where users present themselves to the world, revealing personal details and insights into their lives. We are beginning to understand how some of this information can be utilized to improve the users' experiences with interfaces and with one another. In this paper, we are interested in the personality of users. Personality has been shown to be relevant to many types of interactions, it has been shown to be useful in predicting job satisfaction, professional and romantic relationship success, and even preference for different interfaces. Until now, to accurately gauge users' personalities, they needed to take a personality test. This made it impractical to use personality analysis in many social media domains. In this paper, we present a method by which a user's personality can be accurately predicted through the publicly available information on their Twitter profile. We will describe the type of data collected, our methods of analysis, and the machine learning techniques that allow us to successfully predict personality. We then discuss the implications this has for social media design, interface design, and broader domains.
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Cites
The WEKA data mining software: an update
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THE BIG FIVE PERSONALITY DIMENSIONS AND JOB PERFORMANCE: A META‐ANALYSIS
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An Introduction to the Five‐Factor Model and Its Applications
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Facebook Profiles Reflect Actual Personality, Not Self-Idealization
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“Previous work has shown that the information in users’ Facebook profiles is reflective of their actual personalities, not an “idealized” version of themselves [3].”
BackgroundUsing linguistic cues for the automatic recognition of personality in conversation and text
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Does computer-generated speech manifest personality? an experimental test of similarity-attraction
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Cited by
Private traits and attributes are predictable from digital records of human behavior
Kosinski, M., Stillwell, D., & Graepel, T. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1218772110 · Cited by 2,829
Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach
Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M. E. P., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0073791 · Cited by 1,700
Psychological targeting as an effective approach to digital mass persuasion
Matz, S. C., Kosinski, M., Nave, G., & Stillwell, D. J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1710966114 · Cited by 892
Using machine learning to predict decisions of the European Court of Human Rights
Medvedeva, M., Vols, M., & Wieling, M. (2020) · Artificial Intelligence and Law · doi:10.1007/s10506-019-09255-y · Cited by 429
Wang, W., Harari, G. M., Wang, R., Müller, S. R., Mirjafari, S., Masaba, K., & Campbell, A. T. (2018) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3264951 · Cited by 100
Metadata
- Authors
- Golbeck, J., Robles, C., Edmondson, M., & Turner, K.
- Venue
- 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing
- Publisher
- IEEE
- Published
- 2011-10
- Pages
- 149-156
- OpenAlex
- W2532755691
- Cited by
- 596
- References
- 9
- FWCI
- 15.58
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Golbeck, J., Robles, C., Edmondson, M., & Turner, K. (2011). Predicting Personality from Twitter. 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing. https://doi.org/10.1109/passat/socialcom.2011.33
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