DECIFE：检测Twitter上参与黑市跟踪服务的合谋用户
DECIFE: Detecting Collusive Users Involved in Blackmarket Following Services on Twitter
摘要：
Twitter的流行助长了各种欺诈性用户活动的出现-其中一种活动是通过在短时间内获得大量追随者来人为地提高Twitter个人资料的社会声誉。许多用户希望获得追随者，以增加他们的个人资料的可见性和广泛的受众。这已经引起了一些黑市服务，以一种串通的方式通过网络提供人工关注者的同意和妥协的帐户，以获得巨大的关注。他们的活动很难被发现，因为黑市服务以这样一种方式塑造他们的行为，即这些服务的一部分用户将自己伪装成真正的用户。在本文中，我们提出了DECIFE，一个框架来检测参与生产“以下”活动，通过黑市服务的意图，以获得勾结的追随者作为回报的串通用户。我们首先构建了一个异构的用户推文主题网络，利用追随者/followee的关系和语言属性的用户。然后将异构网络分解为四个不同的子图，以捕获用户之间的语义关系。提出了一种基于注意力的子图聚合网络来学习和组合每个子图的节点表示。最后将组合表示传递到超球学习目标以检测合谋用户。在我们策划的数据集上进行了全面的实验，通过将DECIFE与其他最先进的方法进行比较来验证其有效性。据我们所知，这是第一次尝试检测参与Twitter黑市“跟踪服务”的串通用户。
英文摘要：
The popularity of Twitter has fostered the emergence of various fraudulent user activities - one such activity is to artificially bolster the social reputation of Twitter profiles by gaining a large number of followers within a short time span. Many users want to gain followers to increase the visibility and reach of their profiles to wide audiences. This has provoked several blackmarket services to garner huge attention by providing artificial followers via the network of agreeable and compromised accounts in a collusive manner. Their activity is difficult to detect as the blackmarket services shape their behavior in such a way that users who are part of these services disguise themselves as genuine users. In this paper, we propose DECIFE, a framework to detect collusive users involved in producing 'following' activities through blackmarket services with the intention to gain collusive followers in return. We first construct a heterogeneous user-tweet-topic network to leverage the follower/followee relationships and linguistic properties of a user. The heterogeneous network is then decomposed to form four different subgraphs that capture the semantic relations between the users. An attention-based subgraph aggregation network is proposed to learn and combine the node representations from each subgraph. The combined representation is finally passed on to a hypersphere learning objective to detect collusive users. Comprehensive experiments on our curated dataset are conducted to validate the effectiveness of DECIFE by comparing it with other state-of-the-art approaches. To our knowledge, this is the first attempt to detect collusive users involved in blackmarket 'following services' on Twitter.
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。
