Italian verified accounts X's retweeters during COVID-19 pandemic

Dataset downloaded using official X's APIs (X API - DSA Researcher Application #24-76). The aim of this data collection is to test whether the social impact of an offline event (such as the COVID-19 pandemic) is reflected in how public discourse evolves during the same period. Following the rationale presented in Ref.[1], we considered verified accounts as content creators. To select verified accounts that were actively commenting on the Italian COVID-19 scenario, we identified those present in the dataset from Ref.[2] that were retweeted during the first week of March 2020. We then downloaded all posts that received more than 10 retweets from these users across one-month duration windows every three months, starting in November 2019 and continuing until November 2021. We compiled a list of all retweeters for each time window. The data were further organised as weighted bipartite networks of verified users and their retweeters, where the weight of an edge represents the number of retweets received by a given verified user from each of their retweeters. Furthermore, the data were projected and validated using the procedure defined in Ref.[3]. To comply to X/Twitter's policy, we share the anonymised versions of these last networks. File descriptions:val_net_anonym_vuser_20##-##.txt: Edgelist of the monopartite validated network among verified users. Verified users are anonymised and identified by a 3-digit code. [1] C. Becatti, G. Caldarelli, R. Lambiotte and F. Saracco, “Extracting significant signal of news consumption from social networks: the case of Twitter in Italian political elections”, Palgrave Communications 5, 91 (2019) [2] G. Caldarelli, R. De Nicola, M. Petrocchi, M. Pratelli, F. Saracco, “Flow of online misinformation during the peak of the COVID-19 pandemic in Italy". EPJ Data Sci. 10, 34 (2021) [3] F. Saracco, M. J. Straka, R. Di Clemente, A. Gabrielli, G. Caldarelli and T. Squartini, “Inferring monopartite projections of bipartite networks: an entropy-based approach”, New J. Phys. 19 053022 (2017)