Detection of Fake Users in SMPs Using NLP and Graph Embeddings
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 22 references that are in the map. First posted on arXiv 27 Apr 2021.
Builds on
4 of 22 references.
References · 4
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
- 2016node2vec: Scalable Feature Learning for Networkscited by 1,918
- 2017Online Human-Bot Interactions: Detection, Estimation, and Characterizationcited by 64
- 2017An in-depth characterisation of Bots and Humans on Twittercited by 3
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Citing papers per year: 2023: 1.
1 paper.