Identifying Key-Players in Online Activist Groups on the Facebook Social Network

Mariam Nouh, Jason R. C. Nurse
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引用次数: 23

Abstract

Online social media applications have become an integral part of our everyday life. Not only are they being utilised by individuals and legitimate businesses, but also recently several organised groups, such as activists, hactivists, and cyber-criminals have adopted them to communicate and' spread their ideas. This represents a new source for intelligence gathering for law enforcement for instance, as it allows them an inside look at the behaviour of these previously closed, secretive groups. One possible opportunity with this online data source is to utilise the public exchange of social-media messages to identify key users in such groups. This is particularly important for law enforcement that wants to monitor or interrogate influential people in suspicious groups. In this paper, we utilise Social Network Analysis (SNA) techniques to understand the dynamics of the interaction between users in a Facebook-based activist group. Additionally, we aim to identify the most influential users in the group and infer their relationship strength. We incorporate sentiment analysis to identify users with clear positive and negative influences on the group, this could aid in facilitating a better understanding of the group. We also perform a temporal analysis to correlate online activities with relevant real-life events. Our results show that applying such data analysis techniques on users online behaviour is a powerful tool to predict levels of influence and relationship strength between group members. Finally, we validated our results against the ground truth and found that our approach is very promising at achieving its aims.
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识别Facebook社交网络上在线活动团体的关键人物
在线社交媒体应用程序已经成为我们日常生活中不可或缺的一部分。它们不仅被个人和合法企业使用,最近一些有组织的团体,如活动家、活动家和网络罪犯也采用它们来交流和“传播他们的想法”。例如,这代表了执法部门收集情报的新来源,因为它允许他们深入了解这些以前封闭的秘密组织的行为。这种在线数据源的一个可能的机会是利用社交媒体信息的公开交换来识别这些群体中的关键用户。这对于想要监视或审问可疑群体中有影响力的人的执法部门尤其重要。在这篇论文中,我们利用社会网络分析(SNA)技术来了解一个基于facebook的活动家团体中用户之间互动的动态。此外,我们的目标是确定群体中最具影响力的用户,并推断他们的关系强度。我们结合了情感分析来识别对群体有明确积极和消极影响的用户,这有助于更好地了解群体。我们还进行了时间分析,将在线活动与相关的现实生活事件联系起来。我们的研究结果表明,将此类数据分析技术应用于用户在线行为是预测群组成员之间影响力和关系强度水平的有力工具。最后,我们根据实际情况验证了我们的结果,发现我们的方法在实现其目标方面非常有希望。
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