Detection of Malicious Profiles and Protecting Users in Online Social Networks
Integrity signals
Registry
Cites
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Consumers may be exposed to misleading interactions and content through dishonest accounts, negatively impacting their experience.
“Additionally, consumers may be exposed to misleading interactions and content through dishonest accounts, which could negatively impact their experience.”
From Instagram fake profile detection using an ensemble learning method (2025) · cites this paper as [10]
Cites
A generic statistical approach for spam detection in Online Social Networks
Ahmed, F., & Abulaish, M. (2013) · Computer Communications · doi:10.1016/j.comcom.2013.04.004 · Cited by 199 · see citing works
Detecting Clusters of Fake Accounts in Online Social Networks
Xiao, C., Freeman, D. M., & Hwa, T. (2015) · Proceedings of the 8th ACM Workshop on Artificial Intelligence and Security · doi:10.1145/2808769.2808779 · Cited by 184 · see citing works
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Cited by
Sorted by citations
Shah, A., Varshney, S., & Mehrotra, M. (2024) · SN Computer Science · doi:10.1007/s42979-024-02839-9 · Cited by 16 · see citing works
Instagram fake profile detection using an ensemble learning method
Goyal, B., Gill, N. S., Gulia, P., Alduaiji, N., Shukla, P. K., & J, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-03973-x · Cited by 10 · see citing works
- Authors
- Senthil Raja, M., & Arun Raj, L.
- ORCID
- Senthil Raja, M.
- Venue
- Wireless Personal Communications
- Published
- 2021-01-22
- Volume
- 127
- Issue
- 1
- Pages
- 107-124
- ISSN
- 0929-6212 (ISSN-L)
- 1572-834X
- OpenAlex
- W3121454367
- Cited by
- 25
- References
- 2
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 12 · 2023: 4 · 2022: 2
- FWCI
- 3.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Wireless Personal Communications (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Information Systems › User Authentication and Security Systems
- Keywords
- malicious account detection, social networking sites, fake accounts, privacy preservation, user authentication security, network similarity, feature reduction, user behavior analysis, data mining
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.
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