Social media users drive the spread of misinformation online by sharing posts
that include erroneous information or commenting on controversial topics with
unsubstantiated arguments often in earnest. Work on echo chambers has suggested
that users' perspectives are reinforced through repeated interactions with
like-minded peers, promoted by homophily and bias in information diffusion.
Building on long-standing interest in the social bases of language and
linguistic underpinnings of social behavior, this work explores how
conversations around misinformation are mediated through language use. We
compare a number of linguistic measures, e.g., in-/out-group cues, readability,
and discourse connectives, within and across topics of conversation and user
communities. Our findings reveal increased presence of group identity signals
and processing fluency within echo chambers during discussions of
misinformation. We discuss the specific character of these broader trends
across topics and examine contextual influences.

社交媒体用户通过分享包含错误信息的帖子或对有争议的话题发表无根据的论点来推动在线上的错误信息传播，在回声室中，由同质性和信息传播中的偏见推动，用户的观点通过与志同道合的同伴重复交互得到加强，本研究探讨了如何通过语言使用来调解围绕错误信息的对话，比较了对话和用户社区内外的多种语言度量，如内外群暗示、可读性和话语连接词，我们的研究发现，在讨论错误信息的回声室中增加了群体身份信号和处理流利度，我们还讨论了这些广泛趋势在不同话题和环境影响下的特点。

回音室内部：推特信息误导的语言基础

Inside the echo chamber: Linguistic underpinnings of misinformation on  Twitter

Given the development and abundance of social media, studying the stance of
social media users is a challenging and pressing issue. Social media users
express their stance by posting tweets and retweeting. Therefore, the
homogeneous relationship between users and the heterogeneous relationship
between users and tweets are relevant for the stance detection task. Recently,
graph neural networks (GNNs) have developed rapidly and have been applied to
social media research. In this paper, we crawl a large-scale dataset of the
2020 US presidential election and automatically label all users by manually
tagged hashtags. Subsequently, we propose a bipartite graph neural network
model, DoubleH, which aims to better utilize homogeneous and heterogeneous
information in user stance detection tasks. Specifically, we first construct a
bipartite graph based on posting and retweeting relations for two kinds of
nodes, including users and tweets. We then iteratively update the node's
representation by extracting and separately processing heterogeneous and
homogeneous information in the node's neighbors. Finally, the representations
of user nodes are used for user stance classification. Experimental results
show that DoubleH outperforms the state-of-the-art methods on popular
benchmarks. Further analysis illustrates the model's utilization of information
and demonstrates stability and efficiency at different numbers of layers.

本文利用推文和转发关系构建二分图，使用双重信息处理方法来更新用户的表示，并采用双分图神经网络模型 DoubleH，成功地将用户用户地位分类，证明了 DoubleH 方法在利用信息、稳定性和效率等方面的优越性。