Abstract
This chapter looks at the intricate relationship between journalism and mathematics (big data, algorithms, data mining) as a tool to verify information and fight disinformation. The first section focuses on the relationship current students have with techniques such as big data or artificial intelligence and their ideas on applying them to their profession. The second section maps which universities and researchers in the world are looking into that relationship, how they approach it and where they publish their results. A relevant result is the presence of engineers in those studies, as well as Asian-origin researchers. Finally, we present results that show the increasingly close relationship between different disciplines such as computational linguistics, artificial intelligence and big data to solve the challenge of fake news and disinformation.
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References
Aparici, R., García-Marín, D.: La posverdad. Una Cartografía de los Medios, las redes y la Política. Gedisa, Barcelona (2019)
Asr, F.T., Taboada, M.: Big data and quality data for fake news and misinformation detection. Big Data Soc. 6(1), (2019). https://doi.org/10.1177/2053951719843310
Beskow, D.M., Carley, K.M.: It’s all in a name: Detecting labeling bots by their name. Comput. Math. Organ. Theory 25, 24–35 (2019). https://doi.org/10.1007/s10588-018-09290-1
Bondi, L., Lameri, S., Güera, D., Bestagini, P., Delp, E.J., Tubaro, S.: Tampering detection and localization through clustering of camera-based CNN features. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, 21–26 July, Honolulu (2017). https://doi.org/10.1109/CVPRW.2017.232
Calvo-Rubio, L.M., Ufarte-Ruiz, M.J.: Percepción de docentes universitarios, estudiantes, responsables de innovación y periodistas sobre el uso de inteligencia artificial en periodismo. El Profesional Información 29(1), (2020). https://doi.org/10.3145/epi.2020.ene.09
Choy, M., Chong, M.: Seeing through misinformation: a framework for identifying fake online news. Available at https://arxiv.org/abs/1804.03508 (2018). Accessed 14 Jan 2021
Conroy, N.K., Rubin, V.L., Chen, Y.: Automatic deception detection: methods for finding fake news. Proc. Assoc. Inf. Sci. Technol. 52(1), 1–4 (2016)
Corbetta, P.: Metodologías y técnicas de investigación social. McGraw Hill/Interamericana de España, Madrid (2007)
Dickson, B.: How blockchain helps fight fake news and filter bubbles. The Next Web. Available at https://bit.ly/2OLsNvq (2017). Accessed 20 Jan 2021
Elías, C.: El selfie de Galileo. Software social, político e intelectual del siglo XXI. Península, Barcelona (2015)
Elías, C.: Science on the Ropes. Decline of Scientific Culture in the Era of Fake News. Springer, Cham (2019)
Feng, V.W., Hirst, G.: Detecting deceptive opinions with profile compatibility. In: Mitkov, R., Park, J.C. (eds.) Proceedings of the Sixth International Joint Conference on Natural Language Processing, pp. 338–346. AFNLP, Nagoya (2013)
Ferreira, W., Vlachos, A.: Emergent: a novel data-set for stance classification. In: Knight, K., Nenkova, A., Rambow, O. (eds.) Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1163–1168. ACL, San Diego (2016)
Frías-Navarro, D.: Apuntes de consistencia interna de las puntuaciones de un instrumento de medida. Universidad de Valencia. Available at https://bit.ly/3l7yVKs (2019). Accessed 20 Jan 2021
Iosifidis, P., Nicoli, N.: Digital Democracy, Social Media and Disinformation. Routledge, New York (2020)
Kaliyar, R., Goswami, A., Narang, P., Sinha, S.: FNDNet—A deep convolutional neural network for fake news detection. Cogn. Syst. Res. 61, 32–44 (2020). https://doi.org/10.1016/j.cogsys.2019.12.005
Kwon, S., Cha, M., Jung, K.: Rumor detection over varying time windows. PLoS ONE 12(1), (2017). https://doi.org/10.1371/journal.pone.0168344
Lara-Navarra, P., Falciani, H., Sánchez-Pérez, E., Ferrer-Sapena, A.: Information management in healthcare and environment: towards an automatic system for fake news detection. Int. J. Environ. Res. Public Health 17(3), 1066 (2020). https://doi.org/10.3390/ijerph17031066
Li, Q., Hu, Q., Lu, Y., Yang, Y., Cheng, J.: Multi-level word features based on CNN for fake news detection in cultural communication. Pers. Ubiquit. Comput. 24, 259–272 (2020). https://doi.org/10.1007/s00779-019-01289-y
Lowrey, W.: The emergence and development of news fact checking sites: institutional logics and population ecology. Journalism Stud. 18(3), 376–394 (2019). https://doi.org/10.1080/1461670X.2015.1052537
Papadopoulou, O., Zampoglou, M., Papadopoulos, S., Kompatsiaris, I.: A corpus of debunked and verified user-generated videos. Online Inf. Rev. 43(1), 72–88 (2019). https://doi.org/10.1108/OIR-03-2018-0101
Redondo, M.: Verificación digital para periodistas. Manual contra bulos y desinformación internacional. Editorial UOC, Barcelona (2018)
Tapscott, D., Tapscott, A.: Blockchain Revolution: How the Technology Behind Bitcoin is Changing Money, Business and the World. Random House, New York (2016)
Thorne, J., Vlachos, A., Christodoulopoulos, C., Mittal, A.: FEVER: A large-scale dataset for fact extraction and verification. In: Walker, M., Ji, H., Stent, A. (eds.) Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 809–819. ACL, New Orleans (2018)
Varol, O., Ferrara, E., Clayton, A.D., Menczer, F., Flammini, A.: Online human-bot interactions: Detection, estimation, and characterization. Available at https://arxiv.org/abs/1703.03107 (2017). Accessed 21 Jan 2021
Vlachos, A., Riedel, S.: Fact checking: task definition and dataset construction. In: Danescu-Niculescu-Mizil, C., Eisenstein, J., McKeown, K., Smith, N.A. (eds) Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 18–22. ACL, Baltimore (2014)
Wang, W.Y.: “Liar, liar pants on fire”: a new benchmark dataset for fake news detection. In: Barzilay, R., Kan, M.-Y. (eds.) Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp. 422–426. ACL, Vancouver (2017)
Wooley, S.: The Reality Game: How the Next Wave of Technology Will Break the Truth and What We Can Do About It. Endevour, London (2020)
Zhang, R., Ni, J.: A dense U-net with cross-layer intersection for detection and localization of image forgery. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2982–2986. IEEE, Barcelona (2020)
Funding
This research is also part of the Jean Monnet Chair EU, Disinformation and Fake News, supported by the Erasmus + Program of the European Commission.
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García-Marín, D., Elías, C., Soengas-Pérez, X. (2022). Big Data and Disinformation: Algorithm Mapping for Fact Checking and Artificial Intelligence. In: Vázquez-Herrero, J., Silva-Rodríguez, A., Negreira-Rey, MC., Toural-Bran, C., López-García, X. (eds) Total Journalism. Studies in Big Data, vol 97. Springer, Cham. https://doi.org/10.1007/978-3-030-88028-6_10
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