| _version_ | 1866902105516670976 |
|---|---|
| author | Saracco, Fabio |
| author_facet | Saracco, Fabio |
| contents | <p><strong>Dataset downloaded using official X's APIs (X API - DSA Researcher Application #24-76).</strong></p> <p>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.</p> <p>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.</p> <p><strong>File descriptions:<br></strong>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.</p> <p>[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)</p> <p>[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)</p> <p>[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)</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16537450 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Italian verified accounts X's retweeters during COVID-19 pandemic Saracco, Fabio COVID-19 COVID-19/history Complex analysis <p><strong>Dataset downloaded using official X's APIs (X API - DSA Researcher Application #24-76).</strong></p> <p>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.</p> <p>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.</p> <p><strong>File descriptions:<br></strong>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.</p> <p>[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)</p> <p>[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)</p> <p>[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)</p> |
| title | Italian verified accounts X's retweeters during COVID-19 pandemic |
| topic | COVID-19 COVID-19/history Complex analysis |
| url | https://doi.org/10.5281/zenodo.16537450 |