A SURVEY PAPER ON MECHANISM OF DETECTING BOTS IN TWITTER

The use of bots in social media raises serious concerns about the legitimacy and authenticity of content. There are now various bot detecting solutions available. However, the detection accuracy still needs to be improved. The main goal of this work is to present an autonomous system for detecting and removing bots on social media platforms. The purpose of this investigation is to remove fraudulent accounts, the information associated with them, and the data they transmit, and keep these platforms free of deceptive content. Bot detection and removal will improve the legitimacy of the content displayed on various social media sites. It will also increase the privacy and legitimacy of these sites and their users. The research aims to remove non-genuine accounts, their associated information, and the data that they upload, as well as to rid these platforms of false material. Bot detection and removal will improve the credibility of the content given on various social media sites. It will also increase the privacy and legitimacy of these sites and their users. The bot identification technique based on machine learning algorithms is used in the study. The study's components are data, feature selection, and bot identification. The study uses collected data for web creation and hosting, as well as a machine learning system to detect bots in social media networks. Using machine learning, the suggested method provides a more accurate and effective system for bot detection. The study employs a variety of methodologies and procedures that result in improved bot identification and removal efficiency.