The COVID-19 pandemic has impacted on every human activity and, because of the urgency of finding the proper responses to such an unprecedented emergency, it generated a diffused societal debate. The online version of this discussion was not exempted by the presence of misinformation campaigns, but, differently from what already witnessed in other debates, the COVID-19 -intentional or not- flow of false information put at severe risk the public health, possibly reducing the efficacy of government countermeasures. In this manuscript, we study the effective impact of misinformation in the Italian societal debate on Twitter during the pandemic, focusing on the various discursive communities. In order to extract such communities, we start by focusing on verified users, i.e., accounts whose identity is officially certified by Twitter. We start by considering each couple of verified users and count how many unverified ones interacted with both of them via tweets or retweets: if this number is statically significant, i.e. so great that it cannot be explained only by their activity on the online social network, we can consider the two verified accounts as similar and put a link connecting them in a monopartite network of verified users. The discursive communities can then be found by running a community detection algorithm on this network.

We observe that, despite being a mostly scientific subject, the COVID-19 discussion shows a clear division in what results to be different political groups. We filter the network of retweets from random noise and check the presence of messages displaying URLs. By using the well known browser extension NewsGuard, we assess the trustworthiness of the most recurrent news sites, among those tweeted by the political groups. The impact of low reputable posts reaches the 22.1% in the right and center-right wing community and its contribution is even stronger in absolute numbers, due to the activity of this group: 96% of all non reputable URLs shared by political groups come from this community.

COVID-19 大流行已经影响到每一项人类活动，并且由于迫切需要找到对这种前所未有的紧急情况的适当反应，它引发了广泛的社会辩论。本次讨论的在线版本并没有因错误信息活动的存在而免除，但与其他辩论中已经见证的不同，COVID-19 故意或非虚假信息的流动严重威胁公众健康，可能减少政府对策的有效性。在这份手稿中，我们研究了有效的大流行期间错误信息对 Twitter 上意大利社会辩论的影响，重点关注各种话语社区。为了提取此类社区，我们首先关注经过验证的用户，即身份经过 Twitter 官方认证的帐户。我们首先考虑每对经过验证的用户，并计算有多少未经验证的用户通过推文或转发与他们进行了互动：如果这个数字是静态显着的，即如此之大，以至于不能仅通过他们在在线社交网络上的活动来解释，我们可以将两个经过验证的帐户视为相似，并将连接它们的链接放在经过验证的用户的单方网络中。然后可以通过在该网络上运行社区检测算法来找到话语社区。

我们观察到，尽管 COVID-19 的讨论主要是一个科学主题，但在不同政治团体的结果方面存在明显的分歧。我们从随机噪声中过滤转推网络，并检查是否存在显示 URL 的消息。通过使用著名的浏览器扩展 NewsGuard，我们评估了政治团体发布的那些最经常出现的新闻网站的可信度。低声誉帖子的影响在右翼和中右翼社区中达到 22.1%，其贡献在绝对数量上甚至更大，这是由于该群体的活动：政治团体共享的所有非声誉 URL 的 96% 来自这个社区。