Sustained Online Amplification of COVID-19 Elites in the United States

Gallagher, Ryan J.; Doroshenko, Larissa; Shugars, Sarah; Lazer, David; Welles, Brooke Foucault

doi:10.1177/20563051211024957

Cited by 55 publications

(79 citation statements)

References 52 publications

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“…For individual Twitter users, a more sophisticated measure of influence is used. The user influence is estimated by their retweet h-index [ 40 , 55 ], an adaptation of the well known Hirsch index [ 41 ] to Twitter. The retweet h-index takes into account the number of tweets posted, as well as the impact of individual tweets in terms of retweets.…”

Section: Methodsmentioning

confidence: 99%

Retweet communities reveal the main sources of hate speech

et al. 2022

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We address a challenging problem of identifying main sources of hate speech on Twitter. On one hand, we carefully annotate a large set of tweets for hate speech, and deploy advanced deep learning to produce high quality hate speech classification models. On the other hand, we create retweet networks, detect communities and monitor their evolution through time. This combined approach is applied to three years of Slovenian Twitter data. We report a number of interesting results. Hate speech is dominated by offensive tweets, related to political and ideological issues. The share of unacceptable tweets is moderately increasing with time, from the initial 20% to 30% by the end of 2020. Unacceptable tweets are retweeted significantly more often than acceptable tweets. About 60% of unacceptable tweets are produced by a single right-wing community of only moderate size. Institutional Twitter accounts and media accounts post significantly less unacceptable tweets than individual accounts. In fact, the main sources of unacceptable tweets are anonymous accounts, and accounts that were suspended or closed during the years 2018–2020.

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Section: Methodsmentioning

confidence: 99%

Retweet communities reveal the main sources of hate speech

et al. 2022

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“…This approach complements panel-based longitudinal studies that can be conducted at scale with regards to demographic information such as age, gender, and party affiliation 38 . Here we use influential accounts within each community as sentinel nodes, as previous work has shown that COVID-19 online content generally 20 and COVID-19 misinformation specifically 16 is driven by a small set of influential accounts. We note that other criteria for sentinel node selection, such as a mixture of influential and randomly selected accounts, may also be desirable for detection and characterization of circulating content within communities.…”

Section: Discussionmentioning

confidence: 99%

“…Note that there are many possible ways to select sentinel nodes from a given community. Here we select a subset of influential nodes from each community to use as sentinel nodes, as a majority of Twitter content is driven by a small subset of accounts 20 . Thus, there is some overlap between the selection of sentinel accounts and opinion leadership within the constituent online communities.…”

Section: Introductionmentioning

confidence: 99%

Sentinel node approach to monitoring online COVID-19 misinformation

Osborne

Malloy

Nisbet

et al. 2022

Sci Rep

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Understanding how different online communities engage with COVID-19 misinformation is critical for public health response. For example, misinformation confined to a small, isolated community of users poses a different public health risk than misinformation being consumed by a large population spanning many diverse communities. Here we take a longitudinal approach that leverages tools from network science to study COVID-19 misinformation on Twitter. Our approach provides a means to examine the breadth of misinformation engagement using modest data needs and computational resources. We identify a subset of accounts from different Twitter communities discussing COVID-19, and follow these ‘sentinel nodes’ longitudinally from July 2020 to January 2021. We characterize sentinel nodes in terms of a linked domain preference score, and use a standardized similarity score to examine alignment of tweets within and between communities. We find that media preference is strongly correlated with the amount of misinformation propagated by sentinel nodes. Engagement with sensationalist misinformation topics is largely confined to a cluster of sentinel nodes that includes influential conspiracy theorist accounts. By contrast, misinformation relating to COVID-19 severity generated widespread engagement across multiple communities. Our findings indicate that misinformation downplaying COVID-19 severity is of particular concern for public health response. We conclude that the sentinel node approach can be an effective way to assess breadth and depth of online misinformation penetration.

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“…To the best of our knowledge, the retweet h-index was first used on Twitter data in the context of Brexit, to measure the influence of the Leave and Remain proponents [20]. Later, this measure of influence was termed a retweet h-index [33], a term we also adopt here.…”

Section: Retweet H-index Influencementioning

confidence: 99%

Community evolution in retweet networks

Evkoski,

Mozetic,

Ljubesic

et al. 2021

Preprint

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Communities in social networks often reflect close social ties between their members and their evolution through time. We propose an approach that tracks two aspects of community evolution in retweet networks: flow of the members in, out and between the communities, and their influence. We start with high resolution time windows, and then select several timepoints which exhibit large differences between the communities. For community detection, we propose a two-stage approach. In the first stage, we apply an enhanced Louvain algorithm, called Ensemble Louvain, to find stable communities. In the second stage, we form influence links between these communities, and identify linked super-communities. For the detected communities, we compute internal and external influence, and for individual users, the retweet h-index influence. We apply the proposed approach to three years of Twitter data of all Slovenian tweets. The analysis shows that the Slovenian tweetosphere is dominated by politics, that the left-wing communities are larger, but that the right-wing communities and users exhibit significantly higher impact. An interesting observation is that retweet networks change relatively gradually, despite such events as the emergence of the Covid-19 pandemic or a change of government.

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Sustained Online Amplification of COVID-19 Elites in the United States

Cited by 55 publications

References 52 publications

Retweet communities reveal the main sources of hate speech

Retweet communities reveal the main sources of hate speech

Sentinel node approach to monitoring online COVID-19 misinformation

Community evolution in retweet networks

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