## 2 Related Work
### Bot Detection  
The impact of automated information dissemination on Twitter during big events, such as elections and social media activism, has been addressed by several studies. Bots have been involved in the dissemination of such information, as shown by studies of the 2016 US presidential elections in which the authors created regression models for the diffusion of political messages [5; 25]. Bots have also been seen to advocate for activist participation at extreme rallies to manipulate social networks [26]. These developments raise concerns, as bots' effects on humans have been linked to increased divisiveness and violence in society. To counter the spread of false and damaging information on social media, bot detection platforms have been proposed. There are many well-received supervised bot detection methods in the literature, some of which include features such as text recognition [27], profile features [28], network features [29], and temporal patterns [30]. These methods were created with the help of reference datasets that were annotated to indicate whether an agent is a bot. Botometer, DeBot, and BotHunter are three popular supervised bot detection solutions. These algorithms were trained on massive volumes of data from events, such as the 2016 US election, that were manually labeled with bot/human classifications. A total of 1150 characteristics are retrieved from each Twitter agent and used in Botometer's supervised ensemble classification [31; 32]. Botometer uses features based on users, tweet content, and the network of followers. DeBot [30] uses temporal patterns of account activity, spotting commonalities, differences, and shifting groupings that characterize bot behavior. This technique rests on the premise that a high degree of correlation between the profiles of different social media users is indicative of bot behavior. In [33], BotHunter categorizes Twitter agents using a supervised random forest method via a multi-tiered approach; each approach makes use of additional features as in previous work, from the content to the user to network features, and retrains the supervised model. It uses local categorization evaluation of Twitter agents and accepted tweets from the past as input from a file. Due to the shortage of labeled data for supervised learning (SL) approaches, researchers have been seeking alternatives to SL in order to improve the accuracy of social bot identification using semi-supervised learning (SSL) or unsupervised learning (UL). Recently, ref. [34] proposed a spam detection method using an unsupervised approach. Their model integrates peer acceptance using interaction interest to distinguish genuine users from spam bots. In [35], the authors present a GAN approach with long short-term memory (LSTM) as a method for text-based bot detection. To determine the behaviors of bot samples for a specific classifier, they intended to raise the true-positive rate and lower the false-negative rate. DeeProbot [36] is a hybrid deep learning model that utilizes profile features and handles profile description text using a pre-trained global vector (GLoVe) for word representation. In [37], the author proposed a deep learning framework with the integration of three LSTM models to classify users engaging in interaction activity. [PERSON] et al., (2021) proposed a self-learning framework (SATAR) for the detection of Twitter bots and it was built to be adaptable by pre-training a sizable number of users [38]. In doing so, they attempted to solve the issue of outdated Twitter bot generations. Using self-supervised GNN with reinforcement learning (RL), the authors of [39] proposed a model for bot detection that determines the best multi-hop neighborhood and the number of layers in the GNN architecture. Recent studies have proposed employing GCNNs to improve spam bot detection using relational graph convolution networks (BotRGCN) [40] and GCNNs [41].