During the last two decades, the number of incidents from extremists have increased, so as the use of social media. Research suggests that extremists use social media for reaching their purposes like recruitment, fund raising, and propaganda. Limited research is available to identify rebel users on social media platforms. Therefore, we propose a Supervised Rebel Identification (SRI) framework to identify rebels on Twitter. The framework consists of a novel mechanism to structure the users’ tweets into a directed user graph. This user graph links predicates (verbs) with the subject and object words to understand semantics of the underlying data. We convert the user graph into graph embedding to use these semantics within the machine learning algorithms. Apart from the user graph and its embedding, we propose fourteen other features belonging to tweets’ contents and users’ profiles. For evaluation, we present the first multicultural and multiregional dataset of rebels affiliated with nine rebel movements belonging to five countries. We evaluate the proposed SRI framework against two state-of-the-art baselines. The results show that the SRI framework outperforms the baselines with high accuracy.

在过去的二十年中，由于使用社交媒体，极端分子的事件数量有所增加。研究表明，极端分子利用社交媒体来达到其目的，例如招聘，筹款和宣传。有限的研究可用于识别社交媒体平台上的反叛用户。因此，我们提出了一种监督叛乱者识别（SRI）框架，以在Twitter上识别叛乱者。该框架由一种新颖的机制组成，可以将用户的推文结构化为有向用户图。该用户图将谓词（动词）与主语和宾语联系起来，以理解基础数据的语义。我们将用户图转换为图嵌入，以在机器学习算法中使用这些语义。除了用户图及其嵌入之外，我们提出了14个属于推文内容和用户个人资料的其他功能。为了进行评估，我们提出了与来自五个国家的九个叛乱运动有关联的叛军的第一个多元文化和多区域数据集。我们根据两个最新的基准评估了建议的SRI框架。结果表明，SRI框架在准确性上优于基线。