Shafiei Hossein, Dadlani Aresh
Faculty of Computer Engineering, K. N. Toosi University, Tehran, Iran.
School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan.
J Big Data. 2022;9(1):22. doi: 10.1186/s40537-022-00572-9. Epub 2022 Feb 19.
Online social networks have attracted billions of active users over the past decade. These systems play an integral role in the everyday life of many people around the world. As such, these platforms are also attractive for misinformation, hoaxes, and fake news campaigns which usually utilize social trolls and/or social bots for propagation. Detection of so-called social trolls in these platforms is challenging due to their large scale and dynamic nature where users' data are generated and collected at the scale of multi-billion records per hour. In this paper, we focus on fickle trolls, i.e., a special type of trolling activity in which the trolls change their identity frequently to maximize their social relations. This kind of trolling activity may become irritating for the users and also may pose a serious threat to their privacy. To the best of our knowledge, this is the first work that introduces mechanisms to detect these trolls. In particular, we discuss and analyze troll detection mechanisms on different scales. We prove that the order of centralized single-machine detection algorithm is which is slow and impractical for early troll detection in large-scale social platforms comprising of billions of users. We also prove that the streaming approach where data is gradually fed to the system is not practical in many real-world scenarios. In light of such shortcomings, we then propose a massively parallel detection approach. Rigorous evaluations confirm that our proposed method is at least six times faster compared to conventional parallel approaches.
在过去十年中，在线社交网络吸引了数十亿活跃用户。这些系统在世界各地许多人的日常生活中发挥着不可或缺的作用。因此，这些平台也容易滋生错误信息、恶作剧和虚假新闻活动，这些活动通常利用网络喷子和/或社交机器人进行传播。由于这些平台规模庞大且具有动态性，用户数据以每小时数十亿条记录的规模生成和收集，因此在这些平台上检测所谓的网络喷子具有挑战性。在本文中，我们关注善变的喷子，即一种特殊类型的喷子活动，其中喷子频繁改变身份以最大化他们的社交关系。这种喷子活动可能会让用户感到恼火，也可能对他们的隐私构成严重威胁。据我们所知，这是第一项引入检测这些喷子机制的工作。特别是，我们讨论并分析了不同规模的喷子检测机制。我们证明，集中式单机检测算法的时间复杂度较高，对于包含数十亿用户的大规模社交平台中的早期喷子检测来说速度缓慢且不切实际。我们还证明，在许多实际场景中，数据逐渐输入系统的流式方法并不实用。鉴于这些缺点，我们随后提出了一种大规模并行检测方法。严格的评估证实，我们提出的方法比传统并行方法至少快六倍。
