We present a study on bot detection and its interpretation by assessing the different types of automation that one of the most popular methods for bot detection, Botometer (https://botometer.iuni.iu.edu/), detects. The study is based on the first project to assess the prevalence, influence, and roles of automated accounts in a Twitter follow network on a national scale: the German-speaking Twittersphere. This work in progress allows us to analyse the long-term structural role, impact, and possible audience of bots beyond the context of single events and topics.
Davis, C. A., Varol, O., Ferrara, E., Flammini, A., & Menczer, F. (2016). BotOrNot: A System to Evaluate Social Bots. In Proceedings of the 25th International Conference Companion on World Wide Web, 273–274. https://doi.org/10.1145/2872518.2889302
Salamanos, N., Voudigari, E., & Yannakoudakis, E. J. (2017). A graph exploration method for identifying influential spreaders in complex networks. Applied Network Science, 2(1), 26. https://doi.org/10.1007/s41109-017-0047-y
Varol, O., Ferrara, E., Davis, C. A., Menczer, F., & Flammini, A. (2017). Online Human-Bot Interactions: Detection, Estimation, and Characterization. International AAAI Conference on Web and Social Media. Retrieved from https://aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/view/15587/14817