Abstract

Social Media (SM) has become a popular medium for individuals to share their opinions on various topics, including politics, social issues, and daily affairs. During controversial events such as political elections, active users often proclaim their stance and try to persuade others to support them. However, disparities in participation levels can lead to misperceptions and cause analysts to misjudge the support for each side. For example, current models usually rely on content production and overlook a vast majority of civically engaged users who passively consume information. These “silent users” can significantly impact the democratic process despite being less vocal. Accounting for the stances of this silent majority is critical to improving our reliance on SM to understand and measure social phenomena. Thus, this study proposes and evaluates a new approach for silent users’ stance prediction based on collaborative filtering and Graph Convolutional Networks, which exploits multiple relationships between users and topics. Furthermore, our method allows us to describe users with different stances and online behaviors. We demonstrate its validity using real-world datasets from two related political events. Specifically, we examine user attitudes leading to the Chilean constitutional referendums in 2020 and 2022 through extensive Twitter datasets. In both datasets, our model outperforms the baselines by over 9% at the edge- and the user level. Thus, our method offers an improvement in effectively quantifying the support and creating a multidimensional understanding of social discussions on SM platforms, especially during polarizing events.

中文翻译：

---

揭示沉默的大多数：使用协作过滤和图卷积网络对社交媒体上的被动用户进行立场检测和表征

摘要

社交媒体（SM）已成为个人就各种话题（包括政治、社会问题和日常事务）分享观点的流行媒介。在政治选举等有争议的事件中，活跃用户经常表明自己的立场并试图说服其他人支持他们。然而，参与水平的差异可能会导致误解，并导致分析人士误判各方的支持率。例如，当前的模型通常依赖于内容生产，而忽略了绝大多数被动消费信息的公民参与用户。这些“沉默的用户”尽管声音较小，但仍可以对民主进程产生重大影响。考虑到这个沉默的大多数的立场对于提高我们对 SM 的依赖来理解和衡量社会现象至关重要。因此，本研究提出并评估了一种基于协同过滤和图卷积网络的沉默用户立场预测的新方法，该方法利用了用户和主题之间的多种关系。此外，我们的方法允许我们描述具有不同立场和在线行为的用户。我们使用来自两个相关政治事件的真实世界数据集证明了其有效性。具体来说，我们通过大量 Twitter 数据集研究了导致 2020 年和 2022 年智利宪法公投的用户态度。在这两个数据集中，我们的模型在边缘和用户级别的性能均优于基线 9% 以上。因此，我们的方法在有效量化支持和创建对 SM 平台上的社交讨论的多维理解方面提供了改进，特别是在两极分化事件期间。