ISSN2320-2882
Impact Factor9.17
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ESTD2013
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Paper Title
FakeCheck: A Hybrid Framework For Misinformation Detection Leveraging Logistic Regression And Weighted Community Consensus
Authors
Vijay Shankar , Dipanshu Chaudhary , Surya Raj Sunil , Tejas Sharma , Ashish Raj
Keywords
Fake News Detection, Human-in-the-Loop AI, Logistic Regression, Stylometry, Community Consensus, Explainable Machine Learning, Concept Drift.
Abstract
The rapid growth of digital news platforms has intensified the spread of online misinformation,
rendering manual fact-checking insufficient for real-time verification. While machine learning-based approaches enable scalable detection, they often suffer from limited interpretability, sensitivity to concept drift, and poor handling of contextual nuances such as satire and emerging events. This paper presents FakeCheck, a hybrid misinformation detection framework that integrates interpretable machine learning with a weighted, community
driven human-in-the-loop verification mechanism. The system employs a Logistic Regression classifier trained on TF-IDF representations and engineered stylometric features to generate an initial credibility score. This score is subsequently refined using authenticated community feedback through a mathematically defined weighted consensus formulation. The proposed framework is implemented as a full-stack web application and evaluated on a benchmark fake news dataset. Experimental results show that the standalone classifier achieves strong
baseline performance, while the hybrid framework demonstrates improved robustness in scenarios involving concept drift, satire, and low confidence predictions. The findings highlight that structured integration of human judgment with transparent machine learning models provides a practical and resilient approach for real-time misinformation verification.
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IJCRT — journal style
"FakeCheck: A Hybrid Framework For Misinformation Detection Leveraging Logistic Regression And Weighted Community Consensus", IJCRT - International Journal of Creative Research Thoughts (www.ijcrt.org), ISSN:2320-2882, Vol.14, Issue 2, page no.b945-b958, February 2026, Available :https://ijcrt.org/papers/IJCRT2602215.pdf
APA — 7th edition
Shankar, V., Chaudhary, D., Sunil, S. R., Sharma, T., & Raj, A. (2026). FakeCheck: A Hybrid Framework For Misinformation Detection Leveraging Logistic Regression And Weighted Community Consensus. International Journal of Creative Research Thoughts, 14(2), b945-b958. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602215
MLA — 9th edition
Shankar, Vijay, et al. "FakeCheck: A Hybrid Framework For Misinformation Detection Leveraging Logistic Regression And Weighted Community Consensus." International Journal of Creative Research Thoughts, vol. 14, no. 2, 2026, pp. b945-b958, https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602215.
Chicago — 17th, bibliography
Shankar, Vijay, et al. "FakeCheck: A Hybrid Framework For Misinformation Detection Leveraging Logistic Regression And Weighted Community Consensus." International Journal of Creative Research Thoughts 14, no. 2 (2026): b945-b958. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602215.
Harvard — author–date
Shankar, V. et al. (2026) 'FakeCheck: A Hybrid Framework For Misinformation Detection Leveraging Logistic Regression And Weighted Community Consensus', International Journal of Creative Research Thoughts, 14(2), pp. b945-b958. Available at: https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602215
IEEE — numbered reference
V. Shankar, D. Chaudhary, S. R. Sunil, T. Sharma and A. Raj, "FakeCheck: A Hybrid Framework For Misinformation Detection Leveraging Logistic Regression And Weighted Community Consensus," IJCRT, vol. 14, no. 2, pp. b945-b958, Feb. 2026.
Vancouver — biomedical
Shankar V, Chaudhary D, Sunil SR, Sharma T, Raj A. FakeCheck: A Hybrid Framework For Misinformation Detection Leveraging Logistic Regression And Weighted Community Consensus. IJCRT. 2026 Feb;14(2):b945-b958.
AMA — 11th edition
Shankar V, Chaudhary D, Sunil SR, Sharma T, Raj A. FakeCheck: A Hybrid Framework For Misinformation Detection Leveraging Logistic Regression And Weighted Community Consensus. IJCRT. 2026;14(2):b945-b958. Accessed at https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602215
BibTeX — Zotero, Mendeley, LaTeX
@article{IJCRT2602215,
author = {Vijay Shankar and Dipanshu Chaudhary and Surya Raj Sunil and Tejas Sharma and Ashish Raj},
title = {FakeCheck: A Hybrid Framework For Misinformation Detection Leveraging Logistic Regression And Weighted Community Consensus},
journal = {International Journal of Creative Research Thoughts},
volume = {14},
number = {2},
pages = {b945-b958},
year = {2026},
month = {February},
issn = {2320-2882},
url = {https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602215}
}
RIS — EndNote, RefWorks
TY - JOUR
AU - Shankar, Vijay
AU - Chaudhary, Dipanshu
AU - Sunil, Surya Raj
AU - Sharma, Tejas
AU - Raj, Ashish
TI - FakeCheck: A Hybrid Framework For Misinformation Detection Leveraging Logistic Regression And Weighted Community Consensus
T2 - International Journal of Creative Research Thoughts
JA - IJCRT
VL - 14
IS - 2
PY - 2026
SN - 2320-2882
UR - https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602215
SP - b945-b958
ER -
Issue
Volume 14 Issue 2,
February-2026
Pages : b945-b958
Article Information
- Published In
- International Journal of Creative Research Thoughts — Volume 14, Issue 2, February 2026
- Pages
- b945-b958
- Published
- February 2026
- Registration ID
- IJCRT_301397
- Published Paper ID
- IJCRT2602215
- Research Area
- Science and Technology
- Author Type
- Indian Author
- Location
- New Delhi, Delhi, India
- Peer Review
- Request Peer Review Report
- Licence
- CC BY 4.0 · Open Access
- ISSN & Impact Factor
- 2320-2882 · IF 9.17
- Downloads
- 205
- Permanent Link
- https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602215
- Published Paper PDF
- https://ijcrt.org/papers/IJCRT2602215.pdf
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International Journal of Creative Research Thoughts
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