Abstract
Information is a crucial value nowadays in network digital societies. Therefore, the phenomenon of “fake news” is a serious problem in modern media and communication, e.g. with respect to information spreading within the society about current events and incidents. Fake news are currently a problem for media and broadcasting sector, for citizens, but also for homeland security. In this paper we present and overview the problem of fake news, we show the ideas and solutions for fake news detection, and we present our initial results for one of such approaches based on forged images detection.
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References
Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. J. Econ. Perspect. 31(2), 211–236 (2017). https://doi.org/10.3386/w23089
Euobserver’s Article. https://euobserver.com/foreign/136503. Accessed 24 Mar 2018
Bloomberg’s Article. https://www.livemint.com/Consumer/LKK03QAnhO05wWdT6qCl8O/Facebooks-Journalism-Project-pledges-stronger-media-ties.html. Accessed 06 Apr 2018
Business Insider’s Article. http://www.businessinsider.com/google-jigsaw-perspective-tool-exposes-online-harassment-trolling-2017-2?IR=T. Accessed 28 Mar 2018
The Guardian’s Article. https://www.theguardian.com/technology/2017/feb/08/wikipedia-bans-daily-mail-as-unreliable-source-for-website. Accessed 06 Apr 2018
DW’s Article. http://www.dw.com/en/germany-plans-creation-of-center-of-defense-against-fake-news-report-says/a-36887455. Accessed 25 Mar 2018
BBC’s Article. http://www.bbc.com/news/technology-42510868. Accessed 02 Apr 2018
Rubin, V.L., Chen, Y., Conroy, N.J.: Deception detection for news: three types of fakes. Proc. Assoc. Inf. Sci. Technol. 52(1), 1–4 (2015)
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 Journal, pp. 797–806 (2017)
Rubin, V.L., Conroy, N.J., Chen, Y., Cornwell, S.: Fake news or truth? Using satirical cues to detect potentially misleading news. In: Proceedings of the Second Workshop on Computational Approaches to Deception Detection, pp. 7–17 (2016)
Conroy, N.J., Rubin, V.L., Chen, Y.: Automatic deception detection: methods for finding fake news. Proc. Assoc. Inf. Sci. Technol. 52(1), 1–4 (2015)
Chen, C., Wu, K., Srinivasan, V., Zhang, X.: Battling the internet water army: detection of hidden paid posters. In: Proceedings of 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 116–120. IEEE (2013)
Wang, W.Y.: Liar, Liar Pants on Fire: A New Benchmark Dataset for Fake News Detection. arXiv preprint arXiv:1705.00648 (2017)
Galán-García, P., Puerta, J.G.D.L., Gómez, C.L., Santos, I., Bringas, P.G.: Supervised machine learning for the detection of troll profiles in twitter social network: application to a real case of cyberbullying. Logic J. IGPL 24(1), 42–53 (2016)
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, vol. 10363, pp. 663–671. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-63315-2_58
Huynh, T.K., Huynh, K.V., Le-Tien, T., Nguyen S.C.: A survey on image forgery detection techniques. In: Proceedings of 2015 IEEE RIVF International Conference on Computing & Communication Technologies-Research, Innovation, and Vision for the Future (RIVF), pp. 71–76. IEEE (2015)
Farid, H.: Image forgery detection. IEEE Signal Process. Mag. 26(2), 16–25 (2009)
Amnesty International’s Report. http://www.amnestyusa.org/sites/default/custom-scripts/citizenevidence/. Accessed 12 Mar 2018
CASIA Database. http://forensics.idealtest.org/casiav1/. Accessed 02 Apr 2018
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. Comput. Vis. Image Underst. (CVIU) 110(3), 346–359 (2008)
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Choraś, M., Giełczyk, A., Demestichas, K., Puchalski, D., Kozik, R. (2018). Pattern Recognition Solutions for Fake News Detection. In: Saeed, K., Homenda, W. (eds) Computer Information Systems and Industrial Management. CISIM 2018. Lecture Notes in Computer Science(), vol 11127. Springer, Cham. https://doi.org/10.1007/978-3-319-99954-8_12
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DOI: https://doi.org/10.1007/978-3-319-99954-8_12
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