Abstract
The extreme growth and adoption of Social Media and User Generated Content (UGC) websites, in combination with their poor governance and the lack of quality control over the digital content being published and shared, has led information veracity to a continuous deterioration. Therefore, there is a growing need for reliable information assurance, called by both private and public users and authorities. Due to the popularity of the social media and Internet availability all over the world, anyone can provide a piece of information on the web. This may create a ready channel for spreading false, not verified or confusing information, which may be called ‘fake news’. In order to protect the user from online disinformation, some tools have already been proposed. In this article we have described the available online tools and evaluated them using the same dataset, which was created for this study. We have provided the results proving that fake news detection is an increasingly more pressing, yet a difficult research problem.
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References
Agrawal, R., et al.: Identifying fake news from twitter sharing data: a large-scale study. arXiv preprint arXiv:1902.07207 (2019)
de Alfaro, L., et al.: Reputation systems for news on twitter: a large-scale study. arXiv preprint arXiv:1802.08066 (2018)
Choraś, M., Giełczyk, A., Demestichas, K., Puchalski, D., Kozik, R.: Pattern recognition solutions for fake news detection. In: Saeed, K., Homenda, W. (eds.) CISIM 2018. LNCS, vol. 11127, pp. 130–139. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99954-8_12
Choraś, M., Kozik, R., Renk, R., Hołubowicz, W.: The concept of applying lifelong learning paradigm to cybersecurity. In: Huang, D.-S., Hussain, A., Han, K., Gromiha, M.M. (eds.) ICIC 2017. LNCS (LNAI), vol. 10363, pp. 663–671. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-63315-2_58
https://www.lemonde.fr/verification/. Accessed 18 Apr 2019
ENISA: Strengthening network and information security and protecting against online disinformation (‘fake news’). https://www.enisa.europa.eu/publications/enisa-position-papers-and-opinions/fake-news/. Accessed 18 Apr 2019
http://www.fakenewsai.com/. Accessed 18 Apr 2019
https://fakenewsdetector.org/en. Accessed 20 Apr 2019
http://fakenewsguard.com/index.html. Accessed 18 Apr 2019
https://www.kaggle.com/mrisdal/fake-news. Accessed 26 Apr 2019
Kozik, R., Choras, M., Keller, J.: Balanced efficient lifelong learning (B-ELLA) for cyber attack detection. J. UCS 25(1), 2–15 (2019)
Monti, F., Frasca, F., Eynard, D., Mannion, D., Bronstein, M.M.: Fake news detection on social media using geometric deep learning. arXiv preprint arXiv:1902.06673 (2019)
Reis, J.C., Correia, A., Murai, F., Veloso, A., Benevenuto, F., Cambria, E.: Supervised learning for fake news detection. IEEE Intell. Syst. 34(2), 76–81 (2019)
Ruchansky, N., Seo, S., Liu, Y.: CSI: a hybrid deep model for fake news detection. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 797–806. ACM (2017)
Shu, K., Mahudeswaran, D., Liu, H.: FakeNewsTracker: a tool for fake news collection, detection, and visualization. Comput. Math. Organ. Theory 1–12 (2018)
Shu, K., Mahudeswaran, D., Wang, S., Lee, D., Liu, H.: FakeNewsNet: a data repository with news content, social context and dynamic information for studying fake news on social media. arXiv preprint arXiv:1809.01286 (2018)
https://www.getsurfsafe.com/. Accessed 20 Apr 2019
https://trusted-news.com/. Accessed 20 Apr 2019
Acknowledgement
This work is funded under SocialTruth project, which has received funding from the European Unions Horizon 2020 Research and Innovation Programme under Grant Agreement No. 825477.
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Giełczyk, A., Wawrzyniak, R., Choraś, M. (2019). Evaluation of the Existing Tools for Fake News Detection. In: Saeed, K., Chaki, R., Janev, V. (eds) Computer Information Systems and Industrial Management. CISIM 2019. Lecture Notes in Computer Science(), vol 11703. Springer, Cham. https://doi.org/10.1007/978-3-030-28957-7_13
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DOI: https://doi.org/10.1007/978-3-030-28957-7_13
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