Guillermo Villar-Rodríguez e-mail(Login required) , Mónica Souto-Rico e-mail(Login required) , Alejandro Martín e-mail(Login required)

Main Article Content


Guillermo Villar-Rodríguez e-mail(Login required)
Mónica Souto-Rico e-mail(Login required)
Alejandro Martín e-mail(Login required)



Misinformation has long been a weapon that helps the political, social, and economic interests of different sectors. This became more evident with the transmission of false information in the COVID-19 pandemic, compromising citizens’ health by anti-vaccine recommendations, the denial of the coronavirus and false remedies. Online social networks are the breeding ground for falsehoods and conspiracy theories. Users can share viral misinformation or publish it on their own. This encourages a double analysis of this issue: the need to capture the deluge of false information as opposed to the real one and the study of users’ patterns to interact with that infodemic. As a response to this, our work combines the use of artificial intelligence and journalism through fact-checked false claims to provide an in-depth study of the number of retweets, likes, replies, quotes and repeated texts in posts stating or contradicting misinformation in Twitter. The large sample of tweets was collected and automatically analysed through Natural Language Processing (NLP) techniques, not to give all the attention only to the posts with a big impact but to all the messages contributing to the expansion of false information or its rejection regardless of their virality. This analysis revealed that the diffusion of tweets surrounding coronavirus-related misinformation is not only a domain of viral tweets, but also from posts without interactions, which represent most of the sample, and that there are no big differences between misinformation and its contradiction in general, except for the use of replies.


Misinformation, artificial intelligence, Twitter, COVID-19, Natural Language Processing (NLP)


Ahmed, W., Seguí, F. L., Vidal-Alaball, J., Katz, M. S. & others. (2020). Covid-19 and the “film your hospital” conspiracy theory: social network analysis of twitter data. Journal of Medical Internet Research, 22(10), e22374.

Ahmed, W., Vidal-Alaball, J., Downing, J., Seguí, F. L. & others. (2020). COVID-19 and the 5G conspiracy theory: social network analysis of Twitter data. Journal of Medical Internet Research, 22(5), e19458.

Anjali, B., Reshma, R. & Lekshmy, V. G. (2019). Detection of counterfeit news using machine learning. 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), 1, 1382-1386.

Bello-Orgaz, G., Hernandez-Castro, J. & Camacho, D. (2017). Detecting discussion communities on vaccination in twitter. Future Generation Computer Systems, 66, 125-136.

Brennen, J. S., Simon, F. M., Howard, P. N. & Nielsen, R. K. (2020). Types, sources, and claims of COVID-19 misinformation. Oxford: University of Oxford.

Carlson, M. (2015). The robotic reporter: Automated journalism and the redefinition of labor, compositional forms, and journalistic authority. Digital Journalism, 3(3), 416-431.

Castillo, C., Mendoza, M. & Poblete, B. (2011). Information credibility on twitter. Proceedings of the 20th International Conference on World Wide Web, 675-684.

Choraś Michałand Demestichas, K., Giełczyk, A., Herrero, Á., Ksieniewicz Pawełand Remoundou, K., Urda, D. & Woźniak, M. (2021). Advanced Machine Learning techniques for fake news (online disinformation) detection: A systematic mapping study. Applied Soft Computing, 101, 107050.

Cline, E. H. (2021). 1177 BC. In 1177 BC. Princeton University Press.

Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. ArXiv Preprint ArXiv:1810.04805.

Goel, S., Anderson, A., Hofman, J. & Watts, D. J. (2016). The structural virality of online diffusion. Management Science, 62(1), 180-196.

Graves, L. & Amazeen, M. A. (2019). Fact-checking as idea and practice in Journalism. In Oxford Research Encyclopaedia of Communication. Retrieved from

Harris, Z. S. (1954). Distributional structure. Word, 10(2-3), 146-162.

Herzstein, R. E. (1978). The war that Hitler won: The most infamous propaganda campaign in history. New York: Putnam Publishing Group.

Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H. A. & others. (2021). Bots and misinformation spread on social media: Implications for COVID-19. Journal of Medical Internet Research, 23(5), e26933.

Hirlekar, V. V. & Kumar, A. (2020). Natural language processing based online fake news detection challenge –a detailed review. 2020 5th International Conference on Communication and Electronics Systems (ICCES), 748-754.

Huertas-García, Á., Huertas-Tato, J., Martín, A. & Camacho, D. (2021a). CIVIC-UPM at CheckThat! 2021: integration of transformers in misinformation detection and topic classification. CLEF (Working Notes), 520-530.

Huertas-García, Á., Huertas-Tato, J., Martín, A. & Camacho, D. (2021b). Countering Misinformation Through Semantic-Aware Multilingual Models. International Conference on Intelligent Data Engineering and Automated Learning, 312-323.

Huertas-Tato, J., Martín, A. & Camacho, D. (2021). SILT: Efficient transformer training for inter-lingual inference. arXiv preprint arXiv:2103.09635

Hussain, A., Tahir, A., Hussain, Z., Sheikh, Z., Gogate, M., Dashtipour, K., Ali, A. & Sheikh, A. (2021). Artificial intelligence –enabled analysis of public attitudes on Facebook and Twitter toward Covid-19 vaccines in the United Kingdom and the United States: Observational study. Journal of Medical Internet Research, 23(4), e26627.

Imran, M., Castillo, C., Díaz, F. & Vieweg, S. (2015). Processing social media messages in mass emergency: A survey. ACM Computing Surveys (CSUR), 47(4), 1-38.

Islam, A. K. M. N., Laato, S., Talukder, S. & Sutinen, E. (2020). Misinformation sharing and social media fatigue during COVID-19: An affordance and cognitive load perspective. Technological Forecasting and Social Change, 159, 120201.

Jones, K. S. (1972). A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation.

Jwa, H., Oh, D., Park, K., Kang, J. M. & Lim, H. (2019). exbake: Automatic fake news detection model based on bidirectional encoder representations from transformers (bert). Applied Sciences, 9(19), 4062.

MacCartney, B. (2009). Natural language inference. Standford, CA: Stanford University.

Mantzarlis, A. (2018). Fact-checking 101. Journalism, Fake News & Disinformation: Handbook for Journalism Education and Training, 85-100.

Martín, A., Huertas-Tato, J., Huertas-García, Á., Villar-Rodríguez, G. & Camacho, D. (2021). FacTeR-Check: Semi-automated fact-checking through Semantic Similarity and Natural Language Inference. ArXiv Preprint ArXiv:2110.14532.

Mottola, S. (2020). Las fake news como fenómeno social. Análisis lingüístico y poder persuasivo de bulos en italiano y español. Discurso & Sociedad, 3, 683-706.

Neander, J. & Marlin, R. (2010). Media and Propaganda: The Northcliffe Press and the Corpse Factory Story of World War I. Global Media Journal: Canadian Edition, 3(2).

Nguyen, T. T., Nguyen, Q. V. H., Nguyen, D. T., Hsu, E. B., Yang, S. & Eklund, P. (2020). Artificial intelligence in the battle against coronavirus (COVID-19): a survey and future research directions. ArXiv Preprint ArXiv:2008.07343.

Oshikawa, R., Qian, J. & Wang, W. Y. (2018). A survey on natural language processing for fake news detection. ArXiv Preprint ArXiv:1811.00770.

Parikh, S. B. & Atrey, P. K. (2018). Media-rich fake news detection: A survey. 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), 436-441.

Quandt, T., Frischlich, L., Boberg, S. & Schatto-Eckrodt, T. (2019). Fake news. The International Encyclopedia of Journalism Studies, 1-6.

Raha, T., Indurthi, V., Upadhyaya, A., Kataria, J., Bommakanti, P., Keswani, V. & Varma, V. (2021). Identifying COVID-19 fake news in social media. ArXiv Preprint ArXiv:2101.11954.

Riedel, B., Augenstein, I., Spithourakis, G. P. & Riedel, S. (2017). A simple but tough-to-beat baseline for the Fake News Challenge stance detection task. ArXiv Preprint ArXiv:1707.03264.

Sahoo, S. R. & Gupta, B. B. (2021). Multiple features based approach for automatic fake news detection on social networks using deep learning. Applied Soft Computing, 100, 106983.

Said-Hung, E., Merino-Arribas, M. A. & Martínez, J. (2021). Evolución del debate académico en la Web of Science y Scopus sobre unfaking news (2014-2019). Estudios sobre el Mensaje Periodístico, 27(3), 961-971.

Salaverría, R., Buslón, N., López-Pan, F., León, B., López-Goñi, I. & Erviti, M.-C. (2020). Desinformación en tiempos de pandemia: tipología de los bulos sobre la Covid-19. El Profesional de La Información (EPI), 29(3).

Sharma, K., Qian, F., Jiang, H., Ruchansky, N., Zhang, M. & Liu, Y. (2019). Combating fake news: A survey on identification and mitigation techniques. ACM Transactions on Intelligent Systems and Technology (TIST), 10(3), 1-42.

Shorten, C., Khoshgoftaar, T. M. & Furht, B. (2021). Deep Learning applications for COVID-19. Journal of Big Data, 8(1), 1-54.

Tacchini, E., Ballarin, G., Della Vedova, M. L., Moret, S. & de Alfaro, L. (2017). Some like it hoax: Automated fake news detection in social networks. ArXiv Preprint ArXiv:1704.07506.

Thota, A., Tilak, P., Ahluwalia, S. & Lohia, N. (2018). Fake news detection: a deep learning approach. SMU Data Science Review, 1(3), 10.

Tschiatschek, S., Singla, A., Gómez Rodríguez, M., Merchant, A. & Krause, A. (2018). Fake news detection in social networks via crowd signals. Companion Proceedings of the The Web Conference 2018, 517-524.

Túñez-López, J.-M., Fieiras-Ceide, C. & Vaz-Álvarez, M. (2021). Impact of Artificial Intelligence on Journalism: transformations in the company, products, contents and professional profile. Communication & Society, 34(1), 177-193.

Umer, M., Imtiaz, Z., Ullah, S., Mehmood, A., Choi, G. S. & On, B.-W. (2020). Fake news stance detection using deep learning architecture (CNN-LSTM). IEEE Access, 8, 156695-156706.

Vállez, M. & Codina, L. (2018). Periodismo computacional: evolución, casos y herramientas. Profesional de La Información, 27(4), 759-768.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł. & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.

Viswanath, B., Mislove, A., Cha, M. & Gummadi, K. P. (2009). On the evolution of user interaction in Facebook. Proceedings of the 2nd ACM Workshop on Online Social Networks, 37-42.

Vosoughi, S., Roy, D. & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146-1151.

Wani, A., Joshi, I., Khandve, S., Wagh, V. & Joshi, R. (2021). Evaluating deep learning approaches for covid19 fake news detection. CONSTRAINT@AAAI, 153-163.

Xu, K., Wang, F., Wang, H. & Yang, B. (2019). Detecting fake news over online social media via domain reputations and content understanding. Tsinghua Science and Technology, 25(1), 20-27.

Zhou, X. & Zafarani, R. (2018). Fake news: A survey of research, detection methods, and opportunities. ArXiv Preprint ArXiv:1812.00315, 2.

Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M. & Procter, R. (2018). Detection and resolution of rumours in social media: A survey. ACM Computing Surveys (CSUR), 51(2), 1-36.


Search GoogleScholar


Article Details

Special Issue: Social news diffusion: Platforms, publics, scenarios and dimensions of news sharing