了解激进思想：识别信号以检测Twitter上的极端主义内容
Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter
摘要：
互联网，特别是在线社交网络，改变了恐怖主义和极端主义团体影响和煽动个人的方式。最近的报告显示，这些团体的运作模式首先是让广大受众在网上接触极端主义材料，然后将他们转移到不太开放的网上平台，进一步激进化。因此，识别网上的激进内容对于限制极端主义言论的影响和传播至关重要。在本文中，我们的目的是确定自动检测社交媒体中激进内容的措施。我们确定了几个信号，包括文本，心理和行为，一起允许激进的信息分类。我们的贡献有三个方面：（1）我们分析了极端主义团体发布的宣传材料，并创建了一个基于上下文的激进内容模型;（2）我们建立了一个从这些材料中推断出的心理特性模型;（3）我们在Twitter上评估这些模型，以确定自动识别在线激进推文的可能性。我们的研究结果表明，激进的用户表现出可区分的文本，心理和行为属性。我们发现，心理属性是最显著的特征之一。此外，我们的研究结果表明，使用矢量嵌入功能的文本模型显着提高了TF-IDF功能的检测。我们验证了我们的方法在两个实验实现高精度。我们的研究结果可以作为检测在线激进活动的信号。
英文摘要：
The Internet and, in particular, Online Social Networks have changed the way that terrorist and extremist groups can influence and radicalise individuals. Recent reports show that the mode of operation of these groups starts by exposing a wide audience to extremist material online, before migrating them to less open online platforms for further radicalization. Thus, identifying radical content online is crucial to limit the reach and spread of the extremist narrative. In this paper, our aim is to identify measures to automatically detect radical content in social media. We identify several signals, including textual, psychological and behavioural, that together allow for the classification of radical messages. Our contribution is three-fold: (1) we analyze propaganda material published by extremist groups and create a contextual text-based model of radical content, (2) we build a model of psychological properties inferred from these material, and (3) we evaluate these models on Twitter to determine the extent to which it is possible to automatically identify online radical tweets. Our results show that radical users do exhibit distinguishable textual, psychological, and behavioural properties. We find that the psychological properties are among the most distinguishing features. Additionally, our results show that textual models using vector embedding features significantly improves the detection over TF-IDF features. We validate our approach on two experiments achieving high accuracy. Our findings can be utilized as signals for detecting online radicalization activities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
