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Cited by 183Misinformation and Its ImpactsSpam and Phishing DetectionHate Speech and Cyberbullying Detection

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Content-Based Fake News Detection With Machine and Deep Learning: a Systematic Review

Capuano, Nicola, Fenza, Giuseppe, Loia, Vincenzo, Nota, Francesco David·2023·Neurocomputing
DOI 10.1016/j.neucom.2023.02.005
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  • Humans are capable of providing context and analyzing and tracing sources of information, processes that are still hard to automate.

    “Furthermore, humans are capable of providing context and analyzing and tracing sources of information, processes that are still hard to automate [51].”

    From Human Performance in Deepfake Detection: A Systematic Review (2025) · cites this paper as [51]

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Research works printed but not linked: 49

  1. Zhang, 2020, An overview of online fake news: Characterization, detection, and discussion, Inform. Process. Manage., 57, 10.1016/j.ipm.2019.03.004 · DOI · Paper page
  2. Nasir, 2021, Fake news detection: A hybrid cnn-rnn based deep learning approach, Int. J. Inform. Manage. Data Insights, 1
  3. A. Abedalla, A. Al-Sadi, M. Abdullah, A closer look at fake news detection: A deep learning perspective, in: Proceedings of the 2019 3rd International Conference on Advances in Artificial Intelligence, 2019, pp. 24–28. · DOI
  4. Alameri, 2021, Comparison of fake news detection using machine learning and deep learning techniques, 3rd International Cyber Resilience Conference (CRC), IEEE, 2021, 1
  5. N. Aslam, I. Ullah Khan, F.S. Alotaibi, L.A. Aldaej, A.K. Aldubaikil, Fake detect: A deep learning ensemble model for fake news detection, complexity 2021 (2021). · DOI
  6. Al-Asadi, 2022, Using artificial intelligence against the phenomenon of fake news: a systematic literature review, Combating Fake News with Computational Intelligence Techniques, 39, 10.1007/978-3-030-90087-8_2 · DOI
  7. Lahby, 2022, Online fake news detection using machine learning techniques: A systematic mapping study, Combating Fake News with Computational Intelligence Techniques, 3, 10.1007/978-3-030-90087-8_1 · DOI
  8. Varma, 2021, A systematic survey on deep learning and machine learning approaches of fake news detection in the pre-and post-covid-19 pandemic, Int. J. Intell. Comput. Cybern., 10.1108/IJICC-04-2021-0069 · DOI
  9. A.A.A. Ahmed, A. Aljabouh, P.K. Donepudi, M.S. Choi, Detecting fake news using machine learning: A systematic literature review, arXiv preprint arXiv:2102.04458 (2021). · arXiv
  10. A. Chokshi, R. Mathew, Deep learning and natural language processing for fake news detection: a survey (2020). · DOI
  11. F. Cardoso Durier da Silva, R. Vieira, A.C. Garcia, Can machines learn to detect fake news? a survey focused on social media (2019). · DOI
  12. R. Oshikawa, J. Qian, W.Y. Wang, A survey on natural language processing for fake news detection, arXiv preprint arXiv:1811.00770 (2018). · arXiv
  13. Shu, 2017, Fake news detection on social media: A data mining perspective, ACM SIGKDD explorations newsletter, 19, 22, 10.1145/3137597.3137600 · DOI · Paper page
  14. Biolchini, 2005, Systematic review in software engineering, System engineering and computer science department COPPE/UFRJ, Technical Report ES, 679, 45
  15. W.Y. Wang, liar, liar pants on fire: A new benchmark dataset for fake news detection, arXiv preprint arXiv:1705.00648 (2017). · DOI
  16. Granik, 2017, Fake news detection using naive bayes classifier, IEEE first Ukraine conference on electrical and computer engineering (UKRCON), IEEE, 2017, 900, 10.1109/UKRCON.2017.8100379 · DOI
  17. V. Pérez-Rosas, B. Kleinberg, A. Lefevre, R. Mihalcea, Automatic detection of fake news, arXiv preprint arXiv:1708.07104 (2017). · arXiv
  18. Al Asaad, 2018, A tool for fake news detection, 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), IEEE, 2018, 379
  19. Thota, 2018, Fake news detection: a deep learning approach, SMU Data Sci. Rev., 1, 10
  20. J.Y. Khan, M. Khondaker, T. Islam, A. Iqbal, S. Afroz, A benchmark study on machine learning methods for fake news detection, arXiv preprint arXiv:1905.04749 (2019) 1–14. · arXiv
  21. Reis, 2019, Supervised learning for fake news detection, IEEE Intell. Syst., 34, 76, 10.1109/MIS.2019.2899143 · DOI
  22. Gravanis, 2019, Behind the cues: A benchmarking study for fake news detection, Expert Syst. Appl., 128, 201, 10.1016/j.eswa.2019.03.036 · DOI
  23. Poddar, 2019, Comparison of various machine learning models for accurate detection of fake news, Innovations in Power and Advanced Computing Technologies (i-PACT), volume 1, IEEE, 2019, 1
  24. Bharadwaj, 2019, Fake news detection with semantic features and text mining, Int. J. Natural Language Comput. (IJNLC), 8
  25. K. Shu, L. Cui, S. Wang, D. Lee, H. Liu, defend: Explainable fake news detection, in: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, 2019, pp. 395–405. · DOI
  26. H. Karimi, J. Tang, Learning hierarchical discourse-level structure for fake news detection, arXiv preprint arXiv:1903.07389 (2019). · DOI
  27. H.E. Wynne, Z.Z. Wint, Content based fake news detection using n-gram models, in: Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services, 2019, pp. 669–673. · DOI
  28. Ahmad, 2020, Fake news detection using machine learning ensemble methods, Complexity, 2020, 10.1155/2020/8885861 · DOI
  29. Faustini, 2020, Fake news detection in multiple platforms and languages, Expert Syst. Appl., 158, 10.1016/j.eswa.2020.113503 · DOI
  30. Kaliyar, 2020, Fndnet–a deep convolutional neural network for fake news detection, Cognitive Systems Res., 61, 32, 10.1016/j.cogsys.2019.12.005 · DOI
  31. Kong, 2020, Fake news detection using deep learning, IEEE 10th Symposium on Computer Applications & Industrial Electronics (ISCAIE), IEEE, 2020, 102
  32. S. Vijayaraghavan, Y. Wang, Z. Guo, J. Voong, W. Xu, A. Nasseri, J. Cai, L. Li, K. Vuong, E. Wadhwa, Fake news detection with different models, arXiv preprint arXiv:2003.04978 (2020). · arXiv
  33. Zhou, 2020, Fake news early detection: A theory-driven model, Digital Threats: Res. Practice, 1, 1, 10.1145/3377478 · DOI
  34. Khanam, 2021, Fake news detection using machine learning approaches, vol. 1099, 012040
  35. Choudhary, 2021, Linguistic feature based learning model for fake news detection and classification, Expert Syst. Appl., 169, 10.1016/j.eswa.2020.114171 · DOI
  36. Khan, 2021, A benchmark study of machine learning models for online fake news detection, Mach. Learn. Appl., 4
  37. T. Felber, Constraint 2021: Machine learning models for covid-19 fake news detection shared task, arXiv preprint arXiv:2101.03717 (2021). · arXiv
  38. Verma, 2021, Welfake: word embedding over linguistic features for fake news detection, IEEE Trans. Comput. Soc. Syst., 8, 881, 10.1109/TCSS.2021.3068519 · DOI
  39. S. Gundapu, R. Mamidi, Transformer based automatic covid-19 fake news detection system, arXiv preprint arXiv:2101.00180 (2021). · arXiv
  40. Kaliyar, 2021, Fakebert: Fake news detection in social media with a bert-based deep learning approach, Multimedia Tools Appl., 80, 11765, 10.1007/s11042-020-10183-2 · DOI
  41. Galster, 2013, Variability in software systems–a systematic literature review, IEEE Trans. Software Eng., 40, 282, 10.1109/TSE.2013.56 · DOI
  42. G.C. Santia, J.R. Williams, Buzzface: A news veracity dataset with facebook user commentary and egos, in: Twelfth international AAAI conference on web and social media, 2018. · DOI
  43. Zubiaga, 2016, Analysing how people orient to and spread rumours in social media by looking at conversational threads, PloS one, 11, 10.1371/journal.pone.0150989 · DOI · Paper page
  44. de Melo, 2020, A first public dataset from brazilian twitter and news on covid-19 in portuguese, Data in brief, 32, 10.1016/j.dib.2020.106179 · DOI
  45. F.K.A. Salem, R. Al Feel, S. Elbassuoni, M. Jaber, M. Farah, Fa-kes: A fake news dataset around the syrian war, in: Proceedings of the international AAAI conference on web and social media, volume 13, 2019, pp. 573–582. · DOI
  46. Shu, 2020, Fakenewsnet: A data repository with news content, social context, and spatiotemporal information for studying fake news on social media, Big data, 8, 171, 10.1089/big.2020.0062 · DOI
  47. A.L. e. a. Veronica Perez-Rosas, Bennett Kleinberg, Automatic detection of fake news, International Conference on Computational Linguistics (COLING) (2018).
  48. Mons, 2017, Cloudy, increasingly fair; revisiting the fair data guiding principles for the european open science cloud, Inform. Serv. Use, 37, 49, 10.3233/ISU-170824 · DOI
  49. Z. Zhou, H. Guan, M.M. Bhat, J. Hsu, Fake news detection via nlp is vulnerable to adversarial attacks, arXiv preprint arXiv:1901.09657 (2019). · DOI

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Metadata

Authors
Capuano, N., Fenza, G., Loia, V., & Nota, F. D.
Affiliations
  • University of Basilicata (IT)
  • University of Salerno (IT)
  • Centro Alti Studi per la Difesa (CASD) (IT)
Corresponding author
Capuano, Nicola
ORCID
Venue
Neurocomputing
Publisher
Elsevier BV
Published
2023-04
Volume
530
Pages
91-103
ISSN
0925-2312 (ISSN-L)
DOI
10.1016/j.neucom.2023.02.005
OpenAlex
W4319966514
Cited by
183
References
1
Citations by year
2026: 32 · 2025: 72 · 2024: 55 · 2023: 24
FWCI
9.30
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Neurocomputing (publishedVersion)
  • CINECA IRIS Institutional Research Information System (University of Basilicata) (submittedVersion)
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, content-based detection, automatic fact-checking, machine learning models, deep learning models, feature taxonomy, model performance comparison, misinformation, political news, annotated dataset

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