# Bot Datasets on Twitter: Analysis and Challenges
## 2 Literature Review
### Bot Detection on Twitter  
In the previous section, we verified the importance of users and their relationships on Twitter, potentially generating significant changes in its network [26; 28]; therefore, we need to identify malicious accounts and mitigate their effects and influence, which the Twitter developers believe can help guarantee healthy coexistence and freedom of thought on the platform [32]. Since their discovery and recognition as a serious problem to the platform, researchers on bot detection have been classifying Twitter bots by their motivation [33; 34; 35]. These bots can be content polluters that distribute spam (unwanted electronic messages), attract customers to products or services, and even spread malicious content [36]. There is also a group of bots classified as statistic enhancers, utilized to increase the popularity of a product, person, or company artificially (thus gaining influence over real accounts), generate conversation about a particular topic, or even create false trends [37]. Another group is used as a political influence to change the popular perception of a candidate, infiltrate social discussions, or discourage supporters from voting [34]. Nowadays, these groups present an ongoing threat to the community and the goals of the platform [32]. Research on bot detection is based on the premise that a legitimate human account can be characterized well enough to be clearly distinguished from a bot account, no matter how well it is programmed to act like a genuine human-managed account [38]. Due to the immense potential of bots in modern life, which is so dependant on social networks, new methods are needed for detection and mitigation. [PERSON] et al. [34] presented a group of rules, algorithms, and features used in the literature and online media to identify fake follower accounts (a sub-type of the statistic enhancers) on Twitter. They grouped the features into profile-based, timeline-based, and relationship-based. Their experiments showed that profile-based features offered an accurate prediction while being more cost-efficient than relationship-based features. They also discovered that fake followers do not tweet or use the Twitter API as much as spammers, and concluded that artificial neural networks [39] are promising classification techniques for bot detection. The article ended with the statement that features from social relations analysis were better for detecting fake followers on Twitter. In [35], [PERSON] et al. identified a new problem on Twitter: a novel type of automated accounts for spamming content, called social spambots. This new type of spambot mimicked human behavior on the platform almost identically, making them significantly harder to detect. They proved this by carrying out several experiments using different datasets from Twitter. From them, they concluded that neither the platform, real-world persons, or benchmark algorithms for bot detection could accurately classify this new type of spambot. In the end, they identified a new trend in research that focuses more on analyzing groups of accounts as a whole, rather than evaluating them individually. [PERSON] et al. [40] designed a test to distinguish between bots and humans talking in Spanish. From an initial set of 71 inexpensive features, they performed a filter-based feature selection and ranked them by their importance using four different criteria. Then, they introduced the ranked features to five supervised classifiers, with random forest [41] obtaining the highest average accuracy of 94% using only 19 features. The authors highlighted that while artificial neural networks [39] obtained a considerable accuracy at first (about 90%), the increase of input neurons had minimum returns in terms of accuracy, stagnating at almost 91%. Lastly, they tested the winning classifier with its 19 features against around 5000 unclassified Spanish-speaking accounts, which reported that 13.5% of them were potential bots.