Prospects for the use of applied artificial intelligence technologies in the information verification system of mass media and social media
DOI:
https://doi.org/10.47475/2070-0695-2023-48-2-118-126Keywords:
journalistic education, data verification, fact checking, information technology, artificial intelligence, educational project #СТУДFACTCHECKAbstract
The article discusses theoretical and practical issues related to the use of IT (information technology) in media and social media information verification system. The actual nature of this problem is due to the specifics of the current situation in the information space. The development of modern technologies makes it possible to create and distribute deliberately false content, which in some cases practically cannot be separated from reliable information. This formulates a request for the creation of hybrid fact- checking technologies that would allow combining traditional methods of information verification and the capabilities of applied AI (artificial intelligence). The solution of this problem is necessary, first of all, in the context of protection from falsified and unreliable information, the formation of media literacy of the audience. The article analyzes theoretical approaches to the implementation of this task, presented in the works of foreign and domestic researchers. Based on their conclusions, the authors present a description of their own author’s approach to the process of verifying information using AI technologies, consider examples and possibilities of their implementation in this context. The article also analyzes the methodology for verifying media and social media information within the framework of the international educational project #СТУДFACTCHECK, which has been functioning at the Institute of Philology and Journalism of the Lobachevsky National Research University since 2020 to the present. According to the authors of this study, the experience of the project can be used as a basic model in the process of developing a universal information verification system in Russia at the present stage, including with the use of applied artificial intelligence technologies. The involvement of IT specialists in the creation of methods for automating information verification processes with their subsequent implementation in the process of training future journalists and specialists in the field of mass communication is an urgent task, the implementation of which involves the interaction of IT specialists and socio-humanitarian knowledge.
References
Berezhnaya, V. S. (2020). Voprosi standartizacii faktchekinga v journalistike dannyh. Teoreticheskiy aspekt [Standartized Fact Cheching in Data Journalism. A Theoretical view]. Nauka televidenia, 16.2, 191–209. (In Russ.).
Ilchenko, S. N. (2019). Feikovaya Juornalistika. Specialnyi kurs [Fake Journalism. Special Course]. Moscow: Direkt-Media Publ., 308 p. (In Russ.).
Kuritcyn, A. N., Lemer, L. G., Ashmanov, I. S. & Grebenuk, A. A. (2020). Velikaya Otechestvennaya voina. Falsificacia istorii. Manipulyacii v socialnyh media [The Great Patriotic War. Falsification of history. Manipulation in social media]. Moscow: First Economy Publ., 52 p. (In Russ.).
Lukina, M. M., Zamkov, A. V., Krasheninnikova, M. A. & Kulchitskaya, D. Yu. (2022). Iskusstvennyi intellekt v rossiyskih media i journalistike: k diskussii ob eticheskoy kodificacii. [Artificial Intelligence in the Russian Media and Journalism: The Issue of Ethics]. Voprosy teorii i praktiki zhurnalistiki, vol. 11, 4, 680–694. (In Russ.).
Makarova, L. S., Bureev, A. A. & Makarov, I. V. (2022). Mezhdunarodnyi obrazovatelnyi proekt EUFactcheck v Institute filologii I journalistiki NNGU im. N. I. Lobachevskogo: opit realisacii v sisteme podgotovki studentov po napravleniu «Journalistika» [EUFactcheck, the international educational project at the institute of philology and journalism of Lobachevsky state university of Nizhny Novgorod: experience of system of implementation in the system of training students in the direction of «Journalism»]. Znak: problrmnoe pole mediaobrazovanija, 1 (43), 7–16. (In Russ.).
Makarova, L. S. (2022). Metodicheskie podhodi k formirovaniu navikov verificacii informacii u studentov, obuchaushihsya po napravleniu «Journalistika»: opit realizacii proekta EUFactcheck/#СТУДFACTCHECK v Institute filologii I journalistiki NNGU im. N. I. Lobachevskogo [Methodical approaches to building skills of data verification in students trained under the «Journalism» academic program: experience in implementation of EUFactcheck / # STUDFACTCHECK, the project in the Institute of Philology and Journalism of the Lobachevsky State University]. Chelyabinsky gumanitariy, 1 (58), 70–79. https://www.doi.org/10.47475/2070-0695-2022- 10101. (In Russ.).
Morozova, А. A. & Arsentieva, A. D. (2022). Problemi I perspektivi ispoisovania iskusstvennogo intellekta v sfere mass-media: mnenie rossiyskoi auditoria. [Problems and prospects of the use of artificial intelligence in the sphere of mass media: opinion of the russian audience]. Znak: problemnoe pole mediaobrazovanija, 2 (44), 150–158. https://www.doi.org/10.47475/2070-0695-2022-10219. (In Russ.).
Raspopova, S. S. & Bogdan, E. N. (2018). Feikovie novosti. Informacionnaya mistifikaciya [Fake News. Information Hoax]. Moscow: Aspekt-Press Publ., 112 p. (In Russ.).
Raspopova, S. S. & Simakova, S. I. (2022). Faktcheking kak noviy format samoregulirovania setevoy kommunikacii. [Fact- checking as a new format of self-regulation network communication]. Znak: problemnoe pole mediaobrazovanija, 1 (43), 150–157. https://www.doi.org/10.47475/2070-0695-2022-10116. (In Russ.).
Ryzhova, T. Yu., Glazkov, B. M., Sokolenko, M. V. & Nikiforov, E. A. (2023). Instrumenti II v rukah moshennikov-klassifikacia ugros I sposobi protivodiystvia [Tools of AI in the hands of scammers - classification of threats and ways to counteract]. Moscow: FGUP «GRCHC» Publ., 83 p. (In Russ.).
Tretyakov, A. O., Filatov, O. G., Zhuk, D. V. & Gorlushkin, A. A., Puchkovskaya, A. A. (2018). Metod opredeleniya russkoyasichnyh feikovih novostey s ispolsovaniem elementov iskusstvennogo intellecta [The Method of Identify the Russian-Language Fake News Using Artificial Intelligence]. International Journal of Open Information Technologies, vol. 6, 12, 99–105. (In Russ.).
Chertovskikh, O. O. & Chertovskikh, M. G. (2019). Iskusstvennyi intellect na sluzhbe sovremennoi journalistiki: istoriya, facty, pespectivy azvitiya [Artificial Intelligence in Modern Journalism: History, Facts, Prospects for Development]. Voprosy teorii i praktiki zhurnalistiki, vol. 8, 3, 555–568. (In Russ.).
Bernecker, S., Flowerree, A. K. & Grundmann, Th. (2021). The Epistemology of Fake News. Oxford: Oxford University Press, 374 p.
Botnevik, B., Sakariassen, E. & Setty, V. (2020). BRENDA: Browser Extension for Fake News Detection. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’20), July 25–30, 2020, Virtual Event, China. ACM, New York, NY, USA, P. 1–4, available at: https://dl.acm.org/doi/10.1145/3397271.3401396 (accessed: 21.07.2023).
EUFactcheck. (2020). A Pan-European Project. Methodology and Lessons Learned. Haaga: Helia University of Applied Sciences, 124 p.
Farmer, L. S. J. (2021). Fake News in Context. Routledge, 175 p.
Forgas, J. P. (2021). The Social Psychology of Gullibility. Fake News, Conspiracy Theories, and Irrational Beliefs. Routledge, 363 p.
Giansiracusa, N. (2021). How Algorithms create and prevent fake news. Exploring the impacts of social media, deepfakes, GPT-3, and more. Apress, 239 p.
Greifeneder, R., Jaffe, M. E., Newman, E. J. & Schwarz, N. (2021). The Psychology of Fake News. Accepting, Sharing, and Correcting Misinformation. Routledge, 253 p.
Hasimi, L. & Poniszewska-Marańda, A. (2022). Browser Extension for Detection of Fake News and Disinformation. Papadaki, M., Rupino da Cunha, P., Themistocleous, M., Christodoulou, K. (eds) Information Systems. EMCIS 2022. Lecture Notes in Business Information Processing, vol. 464. Springer, available at: https://doi.org/10.1007/978-3-031-30694-5_16 (accessed: 21.07.2023).
Hassan, N., Li, Ch., Arslan, F. & Tremayne, M. (2017). Toward Automated Fact-Checking: Detecting CheckWorthy Factual Claims by ClaimBuster. KDD ‘17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13–17, 2017. New York: Association for Computing Machinery. 1803–1812.
Journalism, «Fake News» and Disinformation (2018). Paris: UNESCO, 128 p.
Mc Brayer, J. P. (2021). Beyond fake news. Finding the Truth in a World of Misinformation. New York: Ruotledge, 181 p. Miranda, S., Nogueira, D., Mendes, A., Vlachos, A. (2019). Automated Fact Checking in the News Room. Proceedings of the 2019
World Wide Web Conference (WWW ’19), May 13–17, 2019, San Francisco, CA, USA. ACM, New York, NY, USA, available at: https://dl.acm.org/doi/10.1145/3308558.3314135 (accessed: 21.07.2023).
Nakov, P., Corney, D., Hasanain, M., Alam, F., Elsayed, T., Barron-Cedeno, A., Papotti, P., Shaar, Sh., Da San Martino, G. (2021). Automated Fact-Checking for Assisting Human Fact. Doxa: Qatar Computing Research Institute, 250 p.
Deepak, Р., Chakraborty, T., Long, Ch., Santosh, Kumar G. (2021). DATA Science for Fake News. Surveys and Perspectives. Springer, 308 p.
Pond, Ph. (2020). Complexity, Digital Media and Post Truth Politics. A Theory of Interactive Systems. Palgrave Macmillan, 247 p.
Shaar, S., Georgiev, N., Alam, F., Da San Martino, G., Mohamed, A., Nakov, P. (2021). Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document. Doxa: Qatar Computing Research Institute, 308 p.
Shick, N (2020). Deepfakes. The Coming Infocalypse. New York- Boston, 436 p. Video Verification in the Fake News Era. (2019). Springer, 354 p.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Ludmila Makarova, Yuri Batashev
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
