Twitter is a popular social medium for sharing opinions and engaging in topical debates, yet presents a wide spread of misinformation, especially in political debates, from bots and adversarial attacks. The current state-of-the-art methods for detecting humans and bots in Twitter often lack generalizability beyond English. Here, a language-agnostic method to detect real users and their interactions by leveraging network topology from retweets is presented. To that end, the chosen topic is COVID-19 policies in Mexico, which has been considered by users as polemic. Two kinds of network are built: a directed network of retweets; and the co-event network, where a non-directed link between two users exists if they have retweeted the same post in a given time window (projection of a bipartite network). Then, single node properties of these networks, such as the clustering coefficient and the degree, are studied. Three kinds of users are observed: some with a high clustering coefficient but a very small degree, a second group with zero clustering coefficient and a variable degree, and a third group in which the clustering coefficient as a function of the degree decays as a power law. This third group represents $\sim 2\%$ of the users and is characteristic of dynamical networks with feedback. The latter seems to represent strongly interacting followers/followed in a real social network as confirmed by an inspection of such nodes. A percolation analysis of the resulting co-retweet and co-hashtag network reveals the relevance of such weak links, typical of real social human networks. The presented methods are simple to implement in other social media platforms and can be used to mitigate misinformation and conflicts.

Twitter 是一种流行的社交媒体，用于分享观点和参与主题辩论，但它提供了广泛传播的错误信息，尤其是在政治辩论中，来自机器人和对抗性攻击。目前在 Twitter 中检测人类和机器人的最先进方法通常缺乏英语以外的普遍性。在这里，提出了一种通过利用来自转发的网络拓扑来检测真实用户及其交互的与语言无关的方法。为此，选择的主题是墨西哥的 COVID-19 政策，用户认为该政策具有争议性。建立了两种网络：转发的定向网络；和共同事件网络，如果两个用户在给定的时间窗口内转发了相同的帖子，则存在两个用户之间的非定向链接（双向网络的投影）。然后，这些网络的单节点属性，如聚类系数和度，进行研究。观察到三类用户：一些具有高聚类系数但度数非常小，第二组具有零聚类系数和可变度，以及第三组，其中聚类系数作为度数的函数衰减为幂法律。这第三组代表$～2\%$用户，并且是具有反馈的动态网络的特征。后者似乎代表了真实社交网络中强烈互动的追随者/追随者，这通过对此类节点的检查得到证实。对由此产生的共同转发和共同标签网络的渗透分析揭示了这种薄弱环节的相关性，这是真实社会人类网络的典型特征。所提出的方法很容易在其他社交媒体平台上实施，并可用于减轻错误信息和冲突。