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study's robust evaluation framework, incorporating multiple [7] J. Posetti, A. Matthews, A short guide to the history
metrics and visualization techniques, provides a thorough of’fake news’ and disinformation, International Center
analysis of model performance. These findings suggest that for Journalists 7 (2018).
machine learning can be a powerful tool in fighting [8] E. H. Cline, 1177 BC: The year civilization collapsed,
misinformation and maintaining the integrity of information Princeton University Press, 2015.
online. Overall, this research offers valuable insights into
fake news detection, providing a framework that combines [9] J. Neander, R. Marlin, Media and propaganda: The
high accuracy with real-world applicability and emphasizes northcliffe press and the corpse factory story of world
the importance of machine learning in addressing the war i, Global Media Journal 3 (2010) 67.
challenges of misinformation.
[10] R. Herzstein, The most infamous propaganda
References campaign in history, GP Putnam & Sons (1978).
[1] S. B. Parikh, P. K. Atrey, Media-rich fake news [11]
detection: A survey, in: 2018 IEEE Conference on X. Zhang, A. A. Ghorbani, An overview of online fake
news: Characterization, detection, and discussion,
Multimedia Information Processing and Retrieval
Information Processing & Management 57 (2020)
(MIPR), IEEE, 2018, pp. 436–441.
102025.
[2] X. Zhou, R. Zafarani, Fake news: A survey of research, [12]
detection methods, and opportunities, 2018. H. Allcott, M. Gentzkow, Social Media and Fake News
in the 2016 Election, Working Paper 23089, National
arXiv:1812.00315.
Bureau of Economic Research, 2017. URL:
[3] N. K. Conroy, V. L. Rubin, Y. Chen, Automatic deception http://www.nber.org/papers/w23089.
detection: Methods for finding fake news, Proceedings doi:10.3386/w23089.
of the Association for Information Science and [13]
Technology 52 (2015) 1–4. S. Kula, M. Choras, R. Kozik, P. Ksieniewicz, M.
Wo´zniak, Sentiment analysis for fake news detection
[4] A. Zubiaga, A. Aker, K. Bontcheva, M. Liakata, R. by means of neural networks, in: International
Procter, Detection and resolution of rumours in social Conference on Computational Science, Springer, 2020,
media: A survey, ACM Computing Surveys (CSUR) 51 pp. 653–666.
(2018) 1–36.
[14] M. de Cock Buning, A multi-dimensional approach to
[5] K. Sharma, F. Qian, H. Jiang, N. Ruchansky, M. Zhang, Y. disinformation: Report of the independent High level
Liu, Combating fake news: A survey on identification Group on fake news and online disinformation,
and mitigation techniques, ACM Transactions on Publications Office of the European Union, 2018.
Intelligent Systems and Technology (TIST) 10 (2019) [15]
1–42. P. Canada. Parliament. House of Commons. Standing
Committee on Access to Information, Ethics, B.
[6] S. Tschiatschek, A. Singla, M. Gomez Rodriguez, A. Zimmer, Democracy under Threat: Risks and
Merchant, A. Krause, Fake news detection in social Solutions in the Era of Disinformation and Data
networks via crowd signals, in: Companion Monopoly: Report of the Standing Committee on
Proceedings of the The Web Conference 2018, 2018, Access to Information, Privacy and Ethics, House of
pp. 517–524. doi:10.1145/3184558.3188722. Commons= Chambre des communes, Canada, 2018.

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