Mattei Mattia, Caldarelli Guido, Squartini Tiziano, Saracco Fabio
University of Salento, P.zza Tancredi 7, 73100 Lecce, Italy.
IMT School for Advanced Studies, P.zza S. Ponziano 6, 55100 Lucca, Italy.
EPJ Data Sci. 2021;10(1):47. doi: 10.1140/epjds/s13688-021-00301-x. Epub 2021 Sep 9.
The Covid-19 pandemic has had a deep impact on the lives of the entire world population, inducing a participated societal debate. As in other contexts, the debate has been the subject of several d/misinformation campaigns; in a quite unprecedented fashion, however, the presence of false information has seriously put at risk the public health. In this sense, detecting the presence of malicious narratives and identifying the kinds of users that are more prone to spread them represent the first step to limit the persistence of the former ones. In the present paper we analyse the semantic network observed on Twitter during the first Italian lockdown (induced by the hashtags contained in approximately 1.5 millions tweets published between the 23rd of March 2020 and the 23rd of April 2020) and study the extent to which various discursive communities are exposed to d/misinformation arguments. As observed in other studies, the recovered discursive communities largely overlap with traditional political parties, even if the debated topics concern different facets of the management of the pandemic. Although the themes directly related to d/misinformation are a minority of those discussed within our semantic networks, their popularity is unevenly distributed among the various discursive communities.
新冠疫情对全世界人口的生活产生了深刻影响，引发了一场全社会参与的辩论。与其他情况一样，这场辩论成为了几次虚假信息传播活动的主题；然而，以前所未有的方式，虚假信息的存在严重危及了公众健康。从这个意义上说，检测恶意叙事的存在并识别更倾向于传播它们的用户类型，是限制前者持续存在的第一步。在本文中，我们分析了意大利首次封锁期间在推特上观察到的语义网络（由2020年3月23日至2020年4月23日发布的约150万条推文中包含的主题标签引发），并研究了各种话语社区在多大程度上接触到虚假信息论点。正如在其他研究中所观察到的，恢复的话语社区在很大程度上与传统政党重叠，即使辩论的话题涉及疫情管理的不同方面。尽管与虚假信息直接相关的主题在我们的语义网络中讨论的主题中占少数，但其受欢迎程度在各种话语社区中分布不均。
