Detection of Fake Users in SMPs Using NLP and Graph Embeddings
This addresses the issue of fake users on social media platforms, which can be used for competitive advantages, but the approach appears incremental as it combines existing techniques without claiming broad SOTA impact.
The paper tackled the problem of detecting fake and spam user accounts on Twitter by proposing a novel approach that combines Graph Representation Learning and Natural Language Processing techniques, aiming to distinguish between genuine and spam accounts.
Social Media Platforms (SMPs) like Facebook, Twitter, Instagram etc. have large user base all around the world that generates huge amount of data every second. This includes a lot of posts by fake and spam users, typically used by many organisations around the globe to have competitive edge over others. In this work, we aim at detecting such user accounts in Twitter using a novel approach. We show how to distinguish between Genuine and Spam accounts in Twitter using a combination of Graph Representation Learning and Natural Language Processing techniques.