DeepFakE: improving fake news detection using tensor decomposition-based deep neural network
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Combined content and context approach gives better results for fake news detection.
“Results show the combined content and context approach gives better results.”
From Deepfakes: current and future trends (2024) · cites this paper as Kaliyar et al. 2020
Cites
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569 · see citing works
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Deep Learning for Deepfakes Creation and Detection: A Survey
Nguyen, T. T., Nguyen, Q. V. H., Nguyen, D. T., Nguyen, D. T., Huynh-The, T., Nahavandi, S., Nguyen, T. T., Pham, Q.-V., & Nguyen, C. M. (2019) · arXiv · doi:10.48550/arxiv.1909.11573 · Cited by 464
Deep learning for deepfakes creation and detection: A survey
Nguyen, T. T., Nguyen, Q. V. H., Nguyen, D. T., Nguyen, D. T., Huynh-The, T., Nahavandi, S., Nguyen, T. T., Pham, Q.-V., & Nguyen, C. M. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2022.103525 · Cited by 464 · see citing works
“political or religion tensions between countries, to fool public and affect results in election campaigns, or create chaos in financial markets by creating fake news [11].”
BackgroundA Novel Blockchain-Based Deepfake Detection Method Using Federated and Deep Learning Models
Heidari, A., Navimipour, N. J., Dag, H., Talebi, S., & Unal, M. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10255-7 · Cited by 164 · see citing works
Deepfakes: current and future trends
Gambín, Á. F., Yazidi, A., Vasilakos, A., Haugerud, H., & Djenouri, Y. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10679-x · Cited by 145 · see citing works
A Review of Deep Learning-based Approaches for Deepfake Content Detection
Passos, L. A., Jodas, D., Costa, K. A. P., Júnior, L. A. S., Rodrigues, D., Ser, J. D., Camacho*, D., & Papa, J. (2023) · doi:10.22541/au.169735672.27713914/v1 · Cited by 116 · see citing works
- Authors
- Kaliyar, R. K., Goswami, A., & Narang, P.
- Affiliations
- Bennett University (IN)
- Birla Institute of Technology and Science, Pilani (IN)
- Corresponding author
- Narang, Pratik
- Venue
- The Journal of Supercomputing
- Published
- 2020-05-05
- Volume
- 77
- Issue
- 2
- Pages
- 1015-1037
- ISSN
- 0920-8542 (ISSN-L)
- 1573-0484
- OpenAlex
- W3022024046
- Cited by
- 180
- References
- 1
- Citations by year
- 2026: 13 · 2025: 26 · 2024: 42 · 2023: 37 · 2022: 40 · 2021: 18 · 2020: 4
- FWCI
- 33.03
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Journal of Supercomputing (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- fake news detection, tensor decomposition, social media, echo chambers, social environment, news content, user profiles, news credibility, deepfakes, XGBoost ensemble
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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.
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