Fake news agenda in the era of COVID-19: Identifying trends through fact-checking content

Ceron, Wilson; de-Lima-Santos, Mathias-Felipe; Quiles, Marcos G.

doi:10.1016/j.osnem.2020.100116

Cited by 96 publications

(62 citation statements)

References 40 publications

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“…Fake news is spreading across all spheres: science [32][33][34]; art [35,36]; finance [37][38][39]; marketing [40][41][42]; politics [43][44][45]; security [46][47][48]; defense [49,50]; civil protection [51][52][53]; and health [54][55][56][57], including recent sensitive topics such as vaccines [58,59] and COVID-19 [59][60][61][62]. "Fake news" was even named the word of the decade for 2011-2020 by the Macquarie dictionary in 2021 (https://www.macquariedictionary.com.au/blog/article/780/, accessed on 3 June 2021) and Word of the Year 2017 by both the American Dialect Society and Collins English Dictionary (https://en.wikipedia.org/wiki/Word_of_the_year, accessed on 3 June 2021).…”

Section: Fake Newsmentioning

confidence: 99%

Sentiment Analysis for Fake News Detection

et al. 2021

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In recent years, we have witnessed a rise in fake news, i.e., provably false pieces of information created with the intention of deception. The dissemination of this type of news poses a serious threat to cohesion and social well-being, since it fosters political polarization and the distrust of people with respect to their leaders. The huge amount of news that is disseminated through social media makes manual verification unfeasible, which has promoted the design and implementation of automatic systems for fake news detection. The creators of fake news use various stylistic tricks to promote the success of their creations, with one of them being to excite the sentiments of the recipients. This has led to sentiment analysis, the part of text analytics in charge of determining the polarity and strength of sentiments expressed in a text, to be used in fake news detection approaches, either as a basis of the system or as a complementary element. In this article, we study the different uses of sentiment analysis in the detection of fake news, with a discussion of the most relevant elements and shortcomings, and the requirements that should be met in the near future, such as multilingualism, explainability, mitigation of biases, or treatment of multimedia elements.

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Section: Fake Newsmentioning

confidence: 99%

Sentiment Analysis for Fake News Detection

et al. 2021

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“…While fact-checking has been shown to be an effective measure to educate the public and counter the spread of falsehoods, at least based on the assessment of short-term effects of fact-checks on one's beliefs (Dias and Sippitt, 2020;Zhang et al, 2021), the labor intensive manual review process of fact-checking is time consuming and is not scalable due to the sheer volume of both accurate and false posts related to the pandemic circulating on social media (Ceron et al, 2021). Furthermore, the use of automated or semiautomated social media accounts to spread misinformation, also known as social bots (Bessi and Ferrara, 2016;Gonza´lez-Bail on and De Domenico, 2021;Shao et al, 2018;Stella et al, 2018Stella et al, , 2019 has substantially contributed to the scale of this problem.…”

Section: Combatting the Infodemicmentioning

confidence: 99%

Studying the COVID-19 infodemic at scale

et al. 2021

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This special theme issue of Big Data & Society presents leading-edge, interdisciplinary research that focuses on examining how health-related (mis-)information is circulating on social media. In particular, we are focusing on how computational and Big Data approaches can help to provide a better understanding of the ongoing COVID-19 infodemic (overexposure to both accurate and misleading information on a health topic) and to develop effective strategies to combat it.

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“…In the online environment, users and consumers can become victims of fake reviews, satire, deception and farce or they can be manipulated through content defined as fake news. Ceron et al (2021) state that the emergence of social platforms has led to an unprecedented circulation of false information in society [42]. This is more visible in the context of crises such as the COVID-19 pandemic that has tested the social, economic and medical systems around the world.…”

Section: Credibility Of Social Media Informationmentioning

confidence: 99%

“…This is more visible in the context of crises such as the COVID-19 pandemic that has tested the social, economic and medical systems around the world. Unfortunately, in a period of uncertainty and fear, social platforms have intensified the problem by generating an excessive amount of false information related to the pandemic and the measures taken in order to combat it [42].…”

Section: Credibility Of Social Media Informationmentioning

confidence: 99%

Clusters of Skeptical Consumers Based on Technology and AI Acceptance, Perception of Social Media Information and Celebrity Trend Setter

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et al. 2021

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The present consumer is surrounded every day by a huge amount of data and information and is confronted with the need to process the received information. Based on the existing content and the development of user generated content and fake news, consumers develop more and more a skeptical opinion regarding existing media information. In this paper we determine four clusters of consumers based on technology and artificial intelligence (AI) acceptance, skeptical opinion regarding media information, need for validation of received information and the influence of celebrity trend setters. For each of the clusters, we analyze the relationship between the skeptical opinion of consumers related to social media information and some present trends about the development of technologies and AI, the influence of celebrity trend setters and the need to check the received information. The results of the research show that the development of technologies and AI have an influence on the skeptical opinion related to media information for some consumer groups. The celebrity trend setter has a significant influence on the skeptical opinion only for one of the consumer groups. The need for information and news validation is related to the skeptical opinion regarding media information, but in different ways for the determined clusters.

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Fake news agenda in the era of COVID-19: Identifying trends through fact-checking content

Cited by 96 publications

References 40 publications

Sentiment Analysis for Fake News Detection

Sentiment Analysis for Fake News Detection

Studying the COVID-19 infodemic at scale

Clusters of Skeptical Consumers Based on Technology and AI Acceptance, Perception of Social Media Information and Celebrity Trend Setter

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