The proliferation of fake news poses a serious threat to society, as it can misinform and manipulate the public, erode trust in institutions, and undermine democratic processes. To address this issue, we present FakeSwarm, a fake news identification system that leverages the swarming characteristics of fake news. To extract the swarm behavior, we propose a novel concept of fake news swarming characteristics and design three types of swarm features, including principal component analysis, metric representation, and position encoding. We evaluate our system on a public dataset and demonstrate the effectiveness of incorporating swarm features in fake news identification, achieving an f1-score and accuracy of over 97% by combining all three types of swarm features. Furthermore, we design an online learning pipeline based on the hypothesis of the temporal distribution pattern of fake news emergence, validated on a topic with early emerging fake news and a shortage of text samples, showing that swarm features can significantly improve recall rates in such cases. Our work provides a new perspective and approach to fake news detection and highlights the importance of considering swarming characteristics in detecting fake news.

翻译：虚假新闻的泛滥对社会构成严重威胁，因为它可能误导和操纵公众、侵蚀对机构的信任、并破坏民主进程。为解决这一问题，我们提出了FakeSwarm——一种利用虚假新闻群集特征的虚假新闻识别系统。为提取群集行为，我们提出了虚假新闻群集特征的新概念，并设计了三种类型的群集特征，包括主成分分析、度量表示和位置编码。我们在公开数据集上评估了该系统，证明了将群集特征纳入虚假新闻识别的有效性，通过结合所有三种群集特征，F1分数和准确率均超过97%。此外，基于虚假新闻出现的时间分布模式假设，我们设计了一种在线学习流程，并在早期出现虚假新闻且文本样本稀缺的主题上进行了验证，结果表明群集特征在此类情况下能显著提升召回率。我们的工作为虚假新闻检测提供了新的视角与方法，并凸显了在检测虚假新闻时考虑群集特征的重要性。