Profile characteristics of fake Twitter accounts
Open accessAbstract
In online social networks, the audience size commanded by an organization or an individual is a critical measure of that entity’s popularity and this measure has important economic and/or political implications. Such efforts to measure popularity of users or exploit knowledge about their audience are complicated by the presence of fake profiles on these networks. In this study, analysis of 62 million publicly available Twitter user profiles was conducted and a strategy to identify automatically generated fake profiles was established. Using a combination of a pattern-matching algorithm on screen-names and an analysis of update times, a reasonable number (∼0.1% of total users) of highly reliable fake user accounts were identified. Analysis of profile creation times and URLs of these fake accounts revealed their distinct behavior relative to a ground truth data set. The characteristics of friends and followers of users in the two data sets further revealed the very different nature of the two groups. The ratio of number of followers-to-friends for ground truth users was ∼1, consistent with past observations, while the fake profiles had a median ratio ∼30, indicating that the fake users we identified were primarily focused on gathering friends. An analysis of the temporal evolution of accounts over 2 years showed that the friends-to-followers ratio increased over time for fake profiles while they decreased for ground truth users. Our results, thus, suggest that a profile-based approach can be used for identifying a core set of fake online social network users in a time-efficient manner.
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Fake accounts can distort the online landscape by increasing a person's or group's follower count and popularity.
“By increasing a person’s or group’s follower count, fake accounts can significantly distort the online landscape and increase the popularity of the individual or group9.”
From Instagram fake profile detection using an ensemble learning method (2025) · cites this paper as [9]
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- Authors
- Gurajala, S., White, J. S., Hudson, B., Voter, B. R., & Matthews, J. N.
- Affiliations
- Clarkson University (US)
- Corresponding author
- Gurajala, Supraja
- Venue
- Big Data & Society
- Publisher
- SAGE Publications
- Published
- 2016-10-19
- Volume
- 3
- Issue
- 2
- ISSN
- 2053-9517 (ISSN-L)
- OpenAlex
- W2536488151
- Semantic Scholar
- 4a13f10727c233a4741692349c6d49a035c7fd74
- Cited by
- 86
- References
- 20
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 10 · 2023: 14 · 2022: 12 · 2021: 11 · 2020: 14 · 2019: 10 · 2018: 6 · 2017: 2
- FWCI
- 13.39
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Big Data & Society (publishedVersion, cc-by-nc)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- fake accounts, Twitter, social networking sites, update time, user popularity
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