BotNet Detection on Social Media
Aniket Chandrakant Devle, Julia Ann Jose, Abhay Shrinivas, Saraswathula, Shubham Mehta, Siddhant Srivastava, Sirisha Kona, Sudheera, Daggumalli

TL;DR
This paper discusses the importance of detecting social media bots, especially on Twitter, to prevent manipulation of public opinion and disinformation, using web mining techniques.
Contribution
It introduces a novel approach leveraging web mining techniques for social bot detection on social media platforms.
Findings
Effective detection of social bots on Twitter
Reduction in spread of disinformation through detection
Enhanced understanding of social bot behaviors
Abstract
As our reliance on social media platforms and web services increase day by day, exploiters view these platforms as an opportunity to manipulate our thoughts ad actions. These platforms have become an open playground for social bot accounts. Social bots not only learn human conversations, manners, and presence but also manipulate public opinion, act as scammers, manipulate stock markets, and so on. There has been evidence of bots manipulating people's opinions and thoughts which can be a great threat to democracy. Identification and prevention of such campaigns that release or create these bots have become critical. Our goal in this paper is to leverage web mining techniques to help detect fake bots on social media platforms such as Twitter, thereby mitigating the spread of disinformation.
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Taxonomy
TopicsSpam and Phishing Detection · Misinformation and Its Impacts · Network Security and Intrusion Detection
