论文标题
识别和表征在社交媒体中反驳错误信息的活跃公民
Identifying and Characterizing Active Citizens who Refute Misinformation in Social Media
论文作者
论文摘要
在社交媒体中传播错误信息的现象已经开发出一种新的活跃公民形式，他们专注于通过驳斥可能包含错误信息的帖子来解决该问题。自动识别和表征社交媒体中这种活跃公民的行为是计算社会科学中的重要任务，以补充错误信息分析中的研究。在本文中，我们首次研究了不同社交媒体平台（即Twitter和Weibo）和语言（即英语和中文）的这项任务。为此，（1）我们开发并公开提供了一个新的微博用户数据集，该数据集映射到了两个类别之一（即错误信息海报或活跃的公民）中； （2）我们在新的微博数据集和现有的Twitter数据集上评估了一系列监督模型，我们为任务重新利用这些数据集； （3）我们对两个用户类别之间的语言使用差异进行了广泛的分析。
The phenomenon of misinformation spreading in social media has developed a new form of active citizens who focus on tackling the problem by refuting posts that might contain misinformation. Automatically identifying and characterizing the behavior of such active citizens in social media is an important task in computational social science for complementing studies in misinformation analysis. In this paper, we study this task across different social media platforms (i.e., Twitter and Weibo) and languages (i.e., English and Chinese) for the first time. To this end, (1) we develop and make publicly available a new dataset of Weibo users mapped into one of the two categories (i.e., misinformation posters or active citizens); (2) we evaluate a battery of supervised models on our new Weibo dataset and an existing Twitter dataset which we repurpose for the task; and (3) we present an extensive analysis of the differences in language use between the two user categories.
