# Bot Datasets on Twitter: Analysis and Challenges
## 2 Literature Review
### Bot Behavior Identification  
In our previous discussion, we mentioned various benefits a feature model can bring to bot detection research. In this section, we highlight one in particular, which is the basis of this work: the use of their set of features to identify distinct behaviors across multiple types of bot accounts. As [PERSON] et al. [23] state, there is a recognized need to identify bot behavior on Twitter. Many researchers have devoted their efforts to develop mechanisms that detect bots automatically [33; 34; 35; 40], but few of them have centered on understanding their interactions and behaviors [24; 44]. In [44], [PERSON] et al. discovered and studied the behaviors and features of a long-lived botnet of 130 members operating on Twitter that was active for 35 weeks, named the Syrian Social Botnet (SSB). Its purpose was to misroute public opinion from the Syrian civil war and cover tweets related to the ongoing conflict. They conducted three analyses on the dataset of tweets containing the SSB. Their results indicated that the SSB was not programmed to mimic human behavior and had a burst of activity on the last third of its lifespan, getting particular tweets into the top 100 retweeted. The authors concluded that the reason the SSB could have lived for so long and eluded Twitter detection was probably due to the language it was using to tweet (Arabic). [PERSON] et al. [45] analyzed the retweeting behaviors of social bots on the platform. They introduced a scatter diagram named ReTweet-Tweet (RTT) that plots the timestamps of original tweets and their retweets, in which experiments revealed three suspicious behaviors attributed to retweet automation. Then, they proposed Retweet-Buster (RTbust), a bot detection technique that identifies groups of users with synchronized behaviors by exploiting the unique patterns found in the retweeting activity of the automated accounts. The results indicated that RTbust attained an F1 score (harmonic mean of precision and recall) of 87% and also helped recognize two active botnets hidden in their original dataset. In the end, they stated that the future for bot detection research lies in unsupervised learning approaches based on group identification. In [23], [PERSON] et al. studied the behavior and social influence of bots on an altmetric [46] Twitter social network (ATSN) by applying different social network analysis (SNA) techniques. Their analysis revealed bots are highly connected users that influenced 87% of the tweets in the ATSN, meddling with the metrics of scientific documents in the network. They also applied a graph convolutional network technique for bot classification in an ATSN dataset, obtaining an accuracy of 71% and an F1 score of 67%. The authors determined that there are not enough human-labeled collections to improve their model and that the difference between legitimate and social bot accounts is unclear nowadays, even for skilled annotators. [PERSON] and [PERSON] [24] analyzed the behavior of bots and humans over periods of activity (consecutive tweets within 60 min), focusing more on the human perspective by identifying the changes in their behavior that are not present in the bot accounts. From their experiments, they encountered that human behavior comes across a transitory change in their retweets, replies, mentions, and text length over a period of activity, while bots do not experience this. Then, they introduced features related to the periods of activity of the users to improve bot classification into four classifiers, obtaining an AUC of 0.97 for three of them. Finally, they theorized that the reasons for the human behavior changes are derived from the growing tiredness of posting long messages and the increasing exposition to posts of other users, which translates to a greater chance to react. From our previous literature review, we agree with the idea proposed by [PERSON] and [PERSON] [24] that studying bot behavioral dynamics represents an opportunity to improve the state-of-the-art techniques in bot detection.