The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Our Twitter Profiles, Our Selves: Predicting Personality with Twitter
Abstract
Psychological personality has been shown to affect a variety of aspects: preferences for interaction styles in the digital world and for music genres, for example. Consequently, the design of personalized user interfaces and music recommender systems might benefit from understanding the relationship between personality and use of social media. Since there has not been a study between personality and use of Twitter at large, we set out to analyze the relationship between personality and different types of Twitter users, including popular users and influentials. For 335 users, we gather personality data, analyze it, and find that both popular users and influentials are extroverts and emotionally stable (low in the trait of Neuroticism). Interestingly, we also find that popular users are `imaginative' (high in Openness), while influentials tend to be `organized' (high in Conscientiousness). We then show a way of accurately predicting a user's personality simply based on three counts publicly available on profiles: following, followers, and listed counts. Knowing these three quantities about an active user, one can predict the user's five personality traits with a root-mean-squared error below 0.88 on a [1,5] scale. Based on these promising results, we argue that being able to predict user personality goes well beyond our initial goal of informing the design of new personalized applications as it, for example, expands current studies on privacy in social media.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Witten, I. H. (2008) · Cited by 9,143
(1999) · Cited by 7,995
Data mining: practical machine learning tools and techniques with Java implementations
Witten, I. H., & Frank, E. (2002) · ACM SIGMOD Record · doi:10.1145/507338.507355 · Cited by 5,137
The international personality item pool and the future of public-domain personality measures
Goldberg, L. R., Johnson, J. A., Eber, H. W., Hogan, R., Ashton, M. C., Cloninger, C. R., & Gough, H. G. (2006) · Journal of Research in Personality · doi:10.1016/j.jrp.2005.08.007 · Cited by 3,185
The longitudinal course of marital quality and stability: A review of theory, method, and research.
Karney, B. R., & Bradbury, T. N. (1995) · Psychological Bulletin · doi:10.1037/0033-2909.118.1.3 · Cited by 2,608
The Revised NEO Personality Inventory (NEO-PI-R)
Costa, P. T., & McCrae, R. R. (2008) · The SAGE Handbook of Personality Theory and Assessment: Volume 2 — Personality Measurement and Testing · doi:10.4135/9781849200479.n9 · Cited by 1,463
Personality, coping, and coping effectiveness in an adult sample
McCrae, R. R., & Costa, P. T. (1986) · Journal of Personality · doi:10.1111/j.1467-6494.1986.tb00401.x · Cited by 1,113
Who attains social status? Effects of personality and physical attractiveness in social groups.
Anderson, C., John, O. P., Keltner, D., & Kring, A. M. (2001) · Journal of Personality and Social Psychology · doi:10.1037//0022-3514.81.1.116 · Cited by 987
Neurosis and the Social Environment
Henderson, S., Byrne, D. G., & Duncan‐Jones, P. (1982) · Cited by 741
The NEO–PI–3: A More Readable Revised NEO Personality Inventory
McCrae, R. R., Costa, Jr., & Martin, T. A. (2005) · Journal of Personality Assessment · doi:10.1207/s15327752jpa8403_05 · Cited by 700
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
Self-citationDatafication, dataism and dataveillance: Big Data between scientific paradigm and ideology
Van Dijck, J. (2014) · Surveillance & Society · doi:10.24908/ss.v12i2.4776 · Cited by 2,203
Computer-based personality judgments are more accurate than those made by humans
Youyou, W., Kosinski, M., & Stillwell, D. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1418680112 · Cited by 1,086
Self-citationTaxonomy of Risks posed by Language Models
Weidinger, L., Uesato, J., Rauh, M., Griffin, C., Huang, P.-S., Mellor, J., Glaese, A., Cheng, M., Balle, B., Kasirzadeh, A., et al. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533088 · Cited by 764
“Leveraging language processing tools and large public datasets to infer protected and other personal traits is an active area of research [107, 140, 146, 200], despite serious ethical concerns [6, 185].”
BackgroundEthical research protocols for social media health research
Benton, A., Coppersmith, G., & Dredze, M. (2017) · Proceedings of the First ACL Workshop on Ethics in Natural Language Processing · doi:10.18653/v1/w17-1612 · Cited by 219
Metadata
- Authors
- Quercia, D., Kosinski, M., Stillwell, D., & Crowcroft, J.
- 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
- 180-185
- OpenAlex
- W2544158810
- Cited by
- 478
- References
- 35
- FWCI
- 235.52
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Our Twitter Profiles, Our Selves: Predicting Personality with Twitter. https://app.cochat.ai/papers/10.1109/passat/socialcom.2011.26
- The paper
- Quercia, D., Kosinski, M., Stillwell, D., & Crowcroft, J. (2011). Our Twitter Profiles, Our Selves: Predicting Personality with 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.2011.26
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
