# An Evolutionary Computation Approach for Twitter Bot Detection
## 2 Related Work  
In this section, we briefly discuss the most effective spam and bot detection methods that have already been proposed in the literature. The early works were based on the detection and filtering of spam links from URLs, messages, and web pages. [PERSON] et al. [7] combined several metrics such as degree correlations, number of neighbors, and TrustRank [8] to build web spam classifiers. [PERSON] et al. [9] presented a real-time system named \"Monarch\" that crawls URLs as they are submitted to web services, and determines whether the URLs direct to spam websites. [PERSON] et al. [10] built spam classifiers that take advantage of the similarity between spam pages to detect whether an unknown website is spam or legitimate. [PERSON] et al. [11] investigated an approach based on adaptive statistical data compression models for spam filtering. Looking at more recent works, [PERSON] et al. [12] presented a detailed analysis of spam on Twitter to demonstrate the ineffectiveness of blacklists and found that 8% of 25 million URLs point to phishing, malware, and scams listed on popular blacklists, while, on average, more than 90% of visitors view a page before it becomes blacklisted. [PERSON] et al. [13] proposed an online spam filtering system that reconstructs spam messages into campaigns for spam classification. [PERSON] et al. [14] studied the opinion spam and trustworthiness of online opinions in the context of product reviews and implemented a logistic regression classifier to detect spam in these kinds of data. [PERSON] et al. [15] studied deceptive opinion spam and developed a support vector machine (SVM) and a naive Bayes classifier to detect spam. [PERSON] et al. [16] proposed \"WarningBird\", a near real-time detection system for suspicious URLs in Twitter streams that investigates correlations of URL redirect chains extracted from texts of tweets. While most of the aforementioned methods are focused on detecting spam on general messages and web pages, a good percentage of the most recent works are focused on Twitter bot detection. However, general spam detection methods are still largely explored in the recent literature and their results continuously inspire the study and development of novel bot detection strategies. Many research works investigated how to use text from tweets and meta-data to perform bot detection. [PERSON] et al. [17] proposed a classification algorithm consisting of an entropy-based component, a spam detection component, an account properties component, and a decision maker to assess whether a given account is a bot or a human. [PERSON] et al. [18] proposed a bot detection model based on time interval entropy and tweet similarity, where the former was calculated using timestamp collection, while the latter was calculated using uni-gram matching-based similarity. [PERSON] et al. [19] analyzed online users' activities and extracted them from the sets of DNA-inspired strings encoding users' actions. Subsequently, they applied DNA-based analysis methods to detect spam bot profiles. [PERSON] et al. [20] leveraged random string detection applied to user screen names to filter bot accounts on Twitter. [PERSON] et al. [21] presented a set of 14 generic statistical features to identify spam profiles on online social networks (OSNs). Despite most approaches being based on supervised methods, there also exist models that are totally unsupervised. For instance, many works are based on studying how to perform bot and spam classification by analyzing anomalous and suspicious behavior of social profiles. [PERSON] et al. [22] developed a system named \"DeBot\" that consists of a novel lag-sensitive hashing technique that clusters user accounts into correlated sets in near real time. They leveraged cross-user features and activity correlation to detect bots without using labeled data. [PERSON] et al. [23] approached the bot detection problem on Twitter as an anomaly detection problem, where they used 95 one-gram features extracted from the text of the tweets along with user metadata information.