Linguistic drivers of misinformation diffusion on social media during the COVID-19 pandemic

Giandomenico Di Domenico, Annamaria Tuan, Marco Visentin

Research output: Contribution to journalArticlepeer-review

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Abstract

In the wake of the COVID-19 pandemic, unprecedent amounts of fake news and hoax spread on social media. In particular, conspiracy theories argued on the effect of specific new technologies like 5G and misinformation tarnished the reputation of brands like Huawei. Language plays a crucial role in understanding the motivational determinants of social media users in sharing misinformation, as people extract meaning from information based on their discursive resources and their skillset. In this paper, we analyze textual and non-textual cues from a panel of 4923 tweets containing the hashtags #5G and #Huawei during the first week of May 2020, when several countries were still adopting lockdown measures, to determine whether or not a tweet is retweeted and, if so, how much it is retweeted. Overall, through traditional logistic regression and machine learning, we found different effects of the textual and non-textual cues on the retweeting of a tweet and on its ability to accumulate retweets. In particular, the presence of misinformation plays an interesting role in spreading the tweet on the network. More importantly, the relative influence of the cues suggests that Twitter users actually read a tweet but not necessarily they understand or critically evaluate it before deciding to share it on the social media platform.
Original languageEnglish
Pages (from-to)1-19
Number of pages19
JournalItalian Journal of Marketing
Early online date29 May 2021
DOIs
Publication statusEarly online - 29 May 2021

Keywords

  • Covid-19
  • Misinformation
  • Twitter
  • Content analysis
  • Machine Learning

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