Cited by 16Spam and Phishing DetectionMisinformation and Its ImpactsHate Speech and Cyberbullying Detection

Detection of Fake Profiles on Online Social Network Platforms: Performance Evaluation of Artificial Intelligence Techniques

Shah, Akash, Varshney, Sapna, Mehrotra, Monica·2024·SN Computer Science

Integrity signals

Registry

Crossref · 10.1007/s42979-024-02839-9

Cites

44 of 44 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

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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?

  • SMOTE has been extensively used to handle class imbalance in fake account detection tasks to improve model generalizability.

    “Likewise, SMOTE has been extensively used to handle the class imbalance issue in fake account detection tasks to improve model generalizability56.”

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Authors
Shah, A., Varshney, S., & Mehrotra, M.
Affiliations
Jamia Millia Islamia (IN)
Corresponding author
Shah, Akash
Venue
SN Computer Science
Published
2024-04-25
Volume
5
Issue
5
ISSN
2661-8907 (ISSN-L)
OpenAlex
W4395463642
Cited by
16
References
44
Citations by year
2026: 3 · 2025: 7 · 2024: 6
FWCI
6.03
Type
journal article
Language
en
Open access
closed
Locations
SN Computer Science (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
Keywords
fake account detection, fake accounts, social networking sites, deep learning, machine learning, class imbalance, SMOTE, data augmentation, LSTM, XGBoost, benchmark dataset, dataset size

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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