Dataset Information

Flow of online misinformation during the peak of the COVID-19 pandemic in Italy.

ABSTRACT: The COVID-19 pandemic has impacted on every human activity and, because of the urgency of finding the proper responses to such an unprecedented emergency, it generated a diffused societal debate. The online version of this discussion was not exempted by the presence of misinformation campaigns, but, differently from what already witnessed in other debates, the COVID-19 -intentional or not- flow of false information put at severe risk the public health, possibly reducing the efficacy of government countermeasures. In this manuscript, we study the effective impact of misinformation in the Italian societal debate on Twitter during the pandemic, focusing on the various discursive communities. In order to extract such communities, we start by focusing on verified users, i.e., accounts whose identity is officially certified by Twitter. We start by considering each couple of verified users and count how many unverified ones interacted with both of them via tweets or retweets: if this number is statically significant, i.e. so great that it cannot be explained only by their activity on the online social network, we can consider the two verified accounts as similar and put a link connecting them in a monopartite network of verified users. The discursive communities can then be found by running a community detection algorithm on this network. We observe that, despite being a mostly scientific subject, the COVID-19 discussion shows a clear division in what results to be different political groups. We filter the network of retweets from random noise and check the presence of messages displaying URLs. By using the well known browser extension NewsGuard, we assess the trustworthiness of the most recurrent news sites, among those tweeted by the political groups. The impact of low reputable posts reaches the 22.1% in the right and center-right wing community and its contribution is even stronger in absolute numbers, due to the activity of this group: 96% of all non reputable URLs shared by political groups come from this community.

Supplementary information

The online version contains supplementary material available at 10.1140/epjds/s13688-021-00289-4.

SUBMITTER: Caldarelli G

PROVIDER: S-EPMC8258478 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundSocial media platforms are widely used by people suffering from mental illnesses to cope with their conditions. One modality of coping with these conditions is navigating online communities where people can receive emotional support and informational advice. Benefits have been documented in terms of impact on health outcomes. However, the pitfalls are still unknown, as not all content is necessarily helpful or correct. Furthermore, the advent of the COVID-19 pandemic and related problems, such as worsening mental health symptoms, the dissemination of conspiracy narratives, and medical distrust, may have impacted these online communities. The situation in Italy is of particular interest, being the first Western country to experience a nationwide lockdown. Particularly during this challenging time, the beneficial role of community moderators with professional mental health expertise needs to be investigated in terms of uncovering misleading information and regulating communities.ObjectiveThe aim of the proposed study is to investigate the potentially harmful content found in online communities for mental health symptoms in the Italian language. Besides descriptive information about the content that posts and comments address, this study aims to analyze the content from two viewpoints. The first one compares expert-led and peer-led communities, focusing on differences in misinformation. The second one unravels the impact of the COVID-19 pandemic, not by merely investigating differences in topics but also by investigating the needs expressed by community members.MethodsA codebook for the content analysis of Facebook communities has been developed, and a content analysis will be conducted on bundles of posts. Among 14 Facebook groups that were interested in participating in this study, two groups were selected for analysis: one was being moderated by a health professional (n=12,058 members) and one was led by peers (n=5598 members). Utterances from 3 consecutive calendar years will be studied by comparing the months from before the pandemic, the months during the height of the pandemic, and the months during the postpandemic phase (2019-2021). This method permits the identification of different types of misinformation and the context in which they emerge. Ethical approval was obtained by the Università della Svizzera italiana ethics committee.ResultsThe usability of the codebook was demonstrated with a pretest. Subsequently, 144 threads (1534 utterances) were coded by the two coders. Intercoder reliability was calculated on 293 units (19.10% of the total sample; Krippendorff α=.94, range .72-1). Aside from a few analyses comparing bundles, individual utterances will constitute the unit of analysis in most cases.ConclusionsThis content analysis will identify deleterious content found in online mental health support groups, the potential role of moderators in uncovering misleading information, and the impact of COVID-19 on the content.International registered report identifier (irrid)PRR1-10.2196/35347.

Project description:COVID-19 vaccine misinformation is a global threat, and digital and social media support its spread. Addressing Spanish-language vaccine misinformation is critical. In 2021, we began a project to increase vaccine confidence and uptake in the United States by assessing and opposing Spanish-language COVID-19 vaccine misinformation circulating in the United States. Analysts identified trending Spanish-language vaccine misinformation each week, and trained journalists provided communications guidance for addressing the misinformation, which we delivered to community organizations via a weekly newsletter. We identified thematic and geographic trends and highlighted lessons learned to inform future efforts to monitor Spanish-language vaccine misinformation. We collected publicly available Spanish- and English-language COVID-19 vaccine misinformation across various media sources (eg, Twitter, Facebook, news, blogs). Analysts identified top trending vaccine misinformation in the Spanish query and compared it with vaccine misinformation in the English query. Analysts examined misinformation to identify its geographic source and dominant conversation themes. From September 2021 through March 2022, analysts flagged 109 pieces of trending Spanish-language COVID-19 vaccine misinformation. Through this work, we found that Spanish-language vaccine misinformation is easily identifiable. Linguistic networks are not distinct, and vaccine misinformation often circulates across English and Spanish queries. Several websites have outsized influence in promoting Spanish-language vaccine misinformation, suggesting that it may be important to focus on a handful of hyperinfluential accounts and websites. Efforts to address Spanish-language vaccine misinformation must incorporate collaboration with local communities and emphasize community building and empowerment. Ultimately, addressing Spanish-language vaccine misinformation is not an issue of data access and knowledge of how to monitor it; it is an issue of prioritization.

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Flow of online misinformation during the peak of the COVID-19 pandemic in Italy.

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