Clusters of science and health related Twitter users become more isolated during the COVID-19 pandemic

Abstract

COVID-19 represents the most severe global crisis to date whose public conversation can be studied in real time. To do so, we use a data set of over 350 million tweets and retweets posted by over 26 million English speaking Twitter users from January 13 to June 7, 2020. We characterize the retweet network to identify spontaneous clustering of users and the evolution of their interaction over time in relation to the pandemic’s emergence. We identify several stable clusters (super-communities), and are able to link them to international groups mainly involved in science and health topics, national elites, and political actors. The science- and health-related super-community received disproportionate attention early on during the pandemic, and was leading the discussion at the time. However, as the pandemic unfolded, the attention shifted towards both national elites and political actors, paralleled by the introduction of country-specific containment measures and the growing politicization of the debate. Scientific super-community remained present in the discussion, but experienced less reach and became more isolated within the network. Overall, the emerging network communities are characterized by an increased self-amplification and polarization. This makes it generally harder for information from international health organizations or scientific authorities to directly reach a broad audience through Twitter for prolonged time. These results may have implications for information dissemination along the unfolding of long-term events like epidemic diseases on a world-wide scale.

Retweet communities reveal the main sources of hate speech

“…While these results are specific for the Slovenian tweetosphere, there are two lessons important for other domains and languages. One is the concept of super-communities which can be identified after the standard community detection process [ 20 , 44 ], and share several common properties of the constituent communities. Another is the insight that hate speech is not always evenly spread within a super-community, and that it is important to analyze individual communities and different types of users.…”

“…Twitter provides different forms of interactions between the users: follows, mentions, replies, and retweets. The most useful indicator of social ties between the Twitter users are retweets [ 44 , 46 ]. When a user retweets a tweet, it is distributed to all of its followers, just as if it were an originally authored tweet.…”

“…Similarly, the influence of a community can be estimated by the total number of retweets of tweets posted by its members. Retweets within the community indicate internal influence , and retweets outside of the community indicate external influence [ 44 , 54 ].…”

Network Structure and Community Evolution Online: Behavioral and Emotional Changes in Response to COVID-19

“…While Sina Weibo exists as the largest Twitter-like social media in China and has shown of full potential for the public health decision-making via mining of the massive posts produced online, it differs from other social media platforms, e.g., Twitter or Facebook, such that there is no political polarization. It is worth noting that the latter had been found to present polarized network structure and community evolution ( 58 , 59 ). The health- and politics-related discussion received broad attention during the pandemic, and some debates even became increasingly politicized as the pandemic unfolded.…”

“…or Facebook, such that there is no political polarization. It is worth noting that the latter had been found to present polarized network structure and community evolution (58,59). The healthand politics-related discussion received broad attention during the pandemic, and some debates even became increasingly politicized as the pandemic unfolded.…”