分析十年政治谣言特征，揭示其传播趋势与情感规律。
Characteristics of Political Misinformation Over the Past Decade
- 用自然语言处理分析2013-2023年政治谣言特征变化。
- 谣言数量激增，文本与图像类平台占比上升，视频平台开始增长。
- 谣言多含负面情绪，且政治言论整体趋于悲观，适合反虚假信息研究者参考。
尽管虚假信息容易在网络上传播，但可能带来严重的现实后果。为开发自动化工具检测并减轻虚假信息影响，研究人员需使用能适应内容模态（文本、图像、视频）、来源和内容的算法。然而，这些特征随时间动态变化，使构建稳健算法面临挑战。因此，本文利用自然语言处理方法，分析过去十二年（2013–2023）政治虚假信息的共同特征。结果显示，虚假信息近年显著增加，越来越多地通过以文本和图像为主的信息模态传播（如Facebook、Instagram），而包含虚假信息的视频分享平台（如TikTok）也开始上升。此外，虚假信息陈述比真实信息含有更多负面情绪，但准确与不准确信息的情绪均呈下降趋势，表明政治言论整体语气日趋消极。最后，识别出多年重复出现的虚假信息类别，包括科学与医学、犯罪、宗教等引发恐惧或理解困难的主题，以及直接影响民众的政策、选举诚信、经济议题，以及日常生活中的公众人物。这些发现有助于研究人员开发具有时间不变性、可跨时段检测与缓解虚假信息的算法。
原文摘要 · Abstract (English)
Although misinformation tends to spread online, it can have serious real-world consequences. In order to develop automated tools to detect and mitigate the impact of misinformation, researchers must leverage algorithms that can adapt to the modality (text, images and video), the source, and the content of the false information. However, these characteristics tend to change dynamically across time, making it challenging to develop robust algorithms to fight misinformation spread. Therefore, this paper uses natural language processing to find common characteristics of political misinformation over a twelve year period. The results show that misinformation has increased dramatically in recent years and that it has increasingly started to be shared from sources with primary information modalities of text and images (e.g., Facebook and Instagram), although video sharing sources containing misinformation are starting to increase (e.g., TikTok). Moreover, it was discovered that statements expressing misinformation contain more negative sentiment than accurate information. However, the sentiment associated with both accurate and inaccurate information has trended downward, indicating a generally more negative tone in political statements across time. Finally, recurring misinformation categories were uncovered that occur over multiple years, which may imply that people tend to share inaccurate statements around information they fear or don't understand (Science and Medicine, Crime, Religion), impacts them directly (Policy, Election Integrity, Economic) or Public Figures who are salient in their daily lives. Together, it is hoped that these insights will assist researchers in developing algorithms that are temporally invariant and capable of detecting and mitigating misinformation across time.
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