Towards the future of bot detection: A comprehensive taxonomical review and challenges on Twitter/X

IF 4.4 2区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Danish Javed , NZ Jhanjhi , Navid Ali Khan , Sayan Kumar Ray , Alanoud Al Mazroa , Farzeen Ashfaq , Shampa Rani Das
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引用次数: 0

Abstract

Harmful Twitter Bots (HTBs) are widespread and adaptable to a wide range of social network platforms. The use of social network bots on numerous social network platforms is increasing. As the popularity and utility of social networking bots grow, the attacks using social network-based automated accounts are getting more coordinated, resulting in crimes that might endanger democracy, the financial market, and public health. HTB designers develop their bots to elude detection while academics create several algorithms to identify social media bot accounts. This field is active and necessitates ongoing improvement due to the never-ending cat-and-mouse game. X, previously known as Twitter, is among the biggest social network platforms that has been plagued by automated accounts. Even though new research is being conducted to tackle this issue, the number of bots on Twitter keeps on increasing. In this research, we establish a robust theoretical foundation in the continuously evolving domain of Harmful Twitter Bot (HTB) detection by analyzing the existing HTB detection techniques. Our research provides an extensive literature review and introduces an enhanced taxonomy that has the potential to help the scientific community form better generalizations for HTB detection. Furthermore, we discuss this domain's obstacles and open challenges to direct and improve future research. As far as we are aware, this study marks the first comprehensive examination of HTB detection that includes articles published between June 2013 and August 2023. The review's findings include a more thorough classification of detection approaches, a spotlight on ways to spot Twitter bots, and a comparison of recent HTB detection methods. Moreover, we provide a comprehensive list of publicly available datasets for HTB detection. As bots evolve, efforts must be made to raise awareness, equip legitimate users with information, and help future researchers in the field of social network bot detection.
走向僵尸检测的未来:对 Twitter/X 的全面分类审查和挑战
有害的推特机器人(HTB)非常普遍,可适应各种社交网络平台。在众多社交网络平台上使用社交网络机器人的情况越来越多。随着社交网络机器人的普及和实用性的提高,使用基于社交网络的自动账户进行攻击的协调性也越来越高,从而导致可能危及民主、金融市场和公众健康的犯罪。HTB 设计者开发机器人以躲避检测,而学术界则创建了多种算法来识别社交媒体机器人账户。由于猫鼠游戏永无止境,这一领域十分活跃,需要不断改进。前身为 Twitter 的 X 是受自动账户困扰的最大社交网络平台之一。尽管正在开展新的研究来解决这一问题,但 Twitter 上的机器人数量仍在不断增加。在本研究中,我们通过分析现有的有害推特机器人(HTB)检测技术,为不断发展的有害推特机器人(HTB)检测领域奠定了坚实的理论基础。我们的研究提供了广泛的文献综述,并介绍了一种增强型分类法,该分类法有可能帮助科学界对 HTB 检测形成更好的概括。此外,我们还讨论了这一领域的障碍和公开挑战,以指导和改进未来的研究。据我们所知,本研究是首次对 HTB 检测进行全面审查,其中包括 2013 年 6 月至 2023 年 8 月间发表的文章。综述的结论包括对检测方法进行更全面的分类、聚焦发现推特机器人的方法以及比较最近的 HTB 检测方法。此外,我们还提供了用于 HTB 检测的公开可用数据集的综合列表。随着僵尸的不断发展,我们必须努力提高人们的意识,为合法用户提供信息,并为未来社交网络僵尸检测领域的研究人员提供帮助。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computer Networks
Computer Networks 工程技术-电信学
CiteScore
10.80
自引率
3.60%
发文量
434
审稿时长
8.6 months
期刊介绍: Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.
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