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.

Covid-19 大流行对全世界人民的生活产生了深远的影响，引发了广泛的社会辩论。与其他情况一样，这场辩论一直是一些误导/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威胁了公众健康。从这个意义上说，检测恶意叙述的存在并识别更容易传播恶意叙述的用户类型是限制恶意叙述持续存在的第一步。在本文中，我们分析了意大利第一次封锁期间 Twitter 上观察到的语义网络（由 2020 年 3 月 23 日至 2020 年 4 月 23 日期间发布的约 150 万条推文中包含的主题标签引起），并研究了各种话语的程度社区面临 d/错误信息争论。正如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，即使争论的话题涉及大流行管理的不同方面。尽管与 d/错误信息直接相关的主题只占我们语义网络中讨论的少数，但它们的受欢迎程度在各个话语社区中分布不均。