BotNet Detection On Social Media

• URL: http://arxiv.org/abs/2110.05661v1
• Date: Tue, 12 Oct 2021 00:38:51 GMT
• Title: BotNet Detection On Social Media
• Authors: Aniket Chandrakant Devle, Julia Ann Jose, Abhay Shrinivas Saraswathula, Shubham Mehta, Siddhant Srivastava, Sirisha Kona, Sudheera Daggumalli
• Abstract summary: Social media has become an open playground for user (bot) accounts trying to manipulate other users using these platforms. There has been evidence of bots manipulating the election results which can be a great threat to the whole nation and hence the whole world. Our goal is to leverage semantic web mining techniques to identify fake bots or accounts involved in these activities.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Given the popularity of social media and the notion of it being a platform encouraging free speech, it has become an open playground for user (bot) accounts trying to manipulate other users using these platforms. Social bots not only learn human conversations, manners, and presence but also manipulate public opinion, act as scammers, manipulate stock markets, etc. There has been evidence of bots manipulating the election results which can be a great threat to the whole nation and hence the whole world. So identification and prevention of such campaigns that release or create the bots have become critical to tackling it at its source of origin. Our goal is to leverage semantic web mining techniques to identify fake bots or accounts involved in these activities.

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