Social media spammer identification using deep learning

Abstract

Social media has become a part of our lives with countless individuals actively sharing content and connecting with others. However this open platform also presents a challenge, in dealing with misinfor- mation and the spread of information by individuals. Social media platforms face the task of identifying these users and preventing the dissemination of content. In this study the focus is on Twitter as a case example. Explore the use of deep learning techniques to detect fake accounts. We analyze factors such as account age, follower to following ratio, tweet frequency and URL to tweet ratio. Through comparing algorithms the aim is to determine the effective approach, for identifying and controlling fake accounts on Twitter. Ultimately our goal is to provide insights that empower social media platforms to safeguard their users from misinformation and harmful content. By creating an environment that can encourage more responsible and well informed social interactions.