# Bot Datasets on Twitter: Analysis and Challenges
## 2 Literature Review
### Analysis of Twitter's Social Structure  
Due to the increasing role Twitter takes in modern life, researchers have taken an interest in analyzing the structure, users, and interactions on the platform [25]. One of the most important works is the one from [PERSON] et al. [26], which presents the first quantitative analysis on the entire Twitterverse [27] in the literature. The article results in an analysis of the platform's topological features and its potential as a new vehicle for distributing information. From their results, they found that there is a discordance of influence between the number of followers of a user and the number of retweets the user gets. Reciprocated relations also display a tendency of individuals to associate with others of similar opinions. Lastly, by ranking the users by the number of received retweets, the authors exposed the potential impact of the users on the platform. [PERSON] et al. [28] reevaluated the Twitter network presented by [PERSON] et al. [26], gathering users' complete characteristics again to create a new social graph snapshot in 2015, and comparing these two network snapshots (2010 and 2015) thoroughly. Results of the comparison showed that Twitter became a denser but less connected network, with increased reciprocity and a lower average shortest path. The authors also found differences in the popularity ranks and a significant change in its topological characteristics. They concluded by stating this is a consequence of the removal of users on the platform. In [29], [PERSON] et al. published a study of popular hashtags on the platform, analyzing their characteristics, evolution, and measures that appeal to user engagement. The authors first collected the user, tweet, and follower information of accounts participating in Christmas-related hashtags. Then, they represented their Twitter data as a social graph using Gephi software [30] and evaluated the resulting structure with influence, activity,and topological measures. Their results indicate that users with more social connections likely drive their followers into participating in the same hashtag, but the frequency of tweeting of these influential users does not significantly affect their followers. [PERSON] et al. [31] examined the participation of highly connected active users (named elite) in the social structure of Twitter. They constructed an elite social network (comprised of the top 10,000 influential users and their relationships) of the platform and evaluated its characteristics and evolution in two network snapshots over almost three years apart. From their results, they detected that the elite network became a sparser but more connected structure, contrary to the findings of [PERSON] et al. [28]. They also identified these influential users forming communities with a visible identity and theme. Lastly, the authors stated that regular users are inclined to stay in a single elite community, which they believe is a promising property for the clustering of regular users. From these works, we conclude that researchers have identified notable differences in the social structure and the topological characteristics of the platform over the years: first, there is a higher occurrence of bidirectional links between people with similar interests than people with different opinions [26], becoming larger communities around influential users with whom they feel identified [31]. Second, the amount of followers does not directly reflect the influence a user has on the platform, as they are not a guarantee for a high rate of retweets [26; 28]. Third, removing highly connected users can change the topology of the Twitter network [28], as some serve as bridges between user communities [31]. Fourth, users with numerous followers can entice other users into participating in different topics using hashtags [29], potentially becoming promoters of trends in the platform.