RT Journal Article
T1 Virality, only the tip of the iceberg: ways of spread and interaction around COVID-19 misinformation in Twitter
A1 Villar Rodriguez, Guillermo
A1 Souto Rico, Monica Maria
A1 Martín, Alejandro
A1 Villar Rodríguez, Guillermo
A1 Souto Rico, Mónica María
A1 Martín, Alejandro
AB 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.
PB Universidad de Navarra
SN 2386-7876
YR 2022
FD 2022-04-01
LK https://hdl.handle.net/10016/39078
UL https://hdl.handle.net/10016/39078
LA eng
NO This work has been supported by the research project CIVIC: “Intelligent characterisation of the veracity of the information related to COVID-19”, granted by BBVA Foundation Grants for Scientific Research Groups SARS CoV-2 and COVID-19, by the Spanish Ministry of Science and Innovation under FightDIS (PID2020-117263GB-100) and XAI-Disinfodemics (PLEC2021-007681) grants, by Comunidad Autónoma de Madrid under S2018/TCS-4566 grant, by European Commission under IBERIFIER - Iberian Digital Media Research and Fact-Checking Hub (2020-EU-IA-0252), by “Convenio Plurianual with the Universidad Politécnica de Madrid in the actuation line of Programa de Excelencia para el Profesorado Universitario” and by the research project DisTrack: “Tracking disinformation in Online Social Networks through Deep Natural Language Processing”, granted by Barcelona Mobile World CapitalFoundation
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RD 1 sept. 2024