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.

中文翻译：

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Covid-19 流行期间的意大利 Twitter 语义网络

Covid-19 大流行对全世界人民的生活产生了深远的影响，引发了一场参与的社会辩论。与在其他情况下一样，辩论一直是数次 d/错误信息运动的主题；然而，虚假信息的存在以前所未有的方式严重危害了公众健康。从这个意义上说，检测恶意叙述的存在并识别更容易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，我们分析了在第一次意大利封锁期间在 Twitter 上观察到的语义网络（由大约 1. 2020 年 3 月 23 日至 2020 年 4 月 23 日之间发布的 500 万条推文），并研究各种话语社区在多大程度上暴露于 d/错误信息论点。正如在其他研究中所观察到的那样，恢复的话语社区在很大程度上与传统政党重叠，即使辩论的主题涉及大流行管理的不同方面。尽管与 d/misinformation 直接相关的主题是我们语义网络中讨论的主题中的少数，但它们的受欢迎程度在各种话语社区中分布不均。