Yang Kai-Cheng, Ferrara Emilio, Menczer Filippo
Observatory on Social Media, Indiana University Bloomington, Bloomington, IN 47408 USA.
Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292 USA.
J Comput Soc Sci. 2022;5(2):1511-1528. doi: 10.1007/s42001-022-00177-5. Epub 2022 Aug 20.
Social bots have become an important component of online social media. Deceptive bots, in particular, can manipulate online discussions of important issues ranging from elections to public health, threatening the constructive exchange of information. Their ubiquity makes them an interesting research subject and requires researchers to properly handle them when conducting studies using social media data. Therefore, it is important for researchers to gain access to bot detection tools that are reliable and easy to use. This paper aims to provide an introductory tutorial of Botometer, a public tool for bot detection on Twitter, for readers who are new to this topic and may not be familiar with programming and machine learning. We introduce how Botometer works, the different ways users can access it, and present a case study as a demonstration. Readers can use the case study code as a template for their own research. We also discuss recommended practice for using Botometer.
社交机器人已成为在线社交媒体的重要组成部分。尤其是欺骗性机器人，它们能够操纵从选举到公共卫生等重要问题的在线讨论，威胁到信息的建设性交流。它们的普遍存在使其成为一个有趣的研究课题，并且要求研究人员在使用社交媒体数据进行研究时妥善处理它们。因此，研究人员能够使用可靠且易于使用的机器人检测工具非常重要。本文旨在为刚接触该主题且可能不熟悉编程和机器学习的读者提供一份关于Botometer的入门教程，Botometer是一个用于在推特上检测机器人的公共工具。我们介绍了Botometer的工作原理、用户可以访问它的不同方式，并展示了一个案例研究作为示范。读者可以将案例研究代码用作自己研究的模板。我们还讨论了使用Botometer的推荐做法。
