# Bot Datasets on Twitter: Analysis and Challenges
###### Abstract  
The reach and influence of social networks over modern society and its functioning have created new challenges and opportunities to prevent the misuse or tampering of such powerful tools of social interaction. Twitter, a social networking service that specializes in online news and information exchange involving billions of users world-wide, has been infested by bots for several years. In this paper, we analyze both public and private databases from the literature of bot detection on Twitter. We summarize their advantages, disadvantages, and differences, recommending which is more suitable to work with depending on the necessities of the researcher. From this analysis, we present five distinct behaviors in automated accounts exhibited across all the bot datasets analyzed from these databases. We measure their level of presence in each dataset using a radar chart for visual comparison. Finally, we identify four challenges that researchers of bot detection on Twitter have to face when using these databases from the literature.  
bot behavior; bot datasets; twitter; database analysis; database challenges +
Footnote †: journal: Medical  
impact of bot services. While this helped to mitigate the bot presence on Twitter, it is still an ongoing problem.  
Twitter's team reported having 126 million daily active users [16], with the number of monthly active users declining due to their efforts to remove questionable accounts [17]. The platform estimates that 8.5% of all their active accounts are bots [18], where [PERSON] et al. [19] predict that it is around 15%. Over the years, their role as active participants has increased: a study by Sysomos [20] estimated that bots created 32% of Twitter posts from the most active users with more than 150 tweets per day.  
In the literature, bot detection, especially on Twitter, has attracted increasing interest. Searching for the topics on Scopus [21] (Figure 1) in the last ten years (where the most publications has been registered) reveals that significant growth has occurred since 2016, particularly on the topic of bot detection on Twitter (about 850% compared to 2015, from 2 to 19 publications). This increase in interest matches the controversy of the federal election in the United States [22] in the same year (2016). Judging by the surge of publications in the last years, bot detection on the platform is still an open problem that requires special attention due to the potential of these automated accounts to harm the social network and its users.  
Nowadays, researchers [23; 24] of bot detection on Twitter agree that there is a need to discover, analyze, and characterize bot behavior on the platform. The continuous increase in the complexity of bots requires a change of focus that evaluates the dynamics, social interactions, and behaviors of bots rather than searching for individual features to detect them [24].  
In this paper, we perform an analysis of four distinct labeled databases from the literature focused on bot detection. We compare the human and bot datasets (classes) on these databases using their extracted features and represent them as graphs for visual comparison to expose distinct behaviors of bot accounts that can be used to detect them individually or  
Figure 1: The number of publications on bot detection and bot detection on Twitter for the last ten years in Scopus. We observed a significant increase in interest in 2016 matching the controversy of the U.S. federal election. The data for 2020 were gathered until September.  
as a group. We define the behavior of a bot as the series of decisions and actions they take on the platform to accomplish their goal, programmed by the botmaster (owner).