Publication Date
Spring 2024
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Katerina Potika
Second Advisor
Philip Heller
Third Advisor
William Andreopoulos
Keywords
Bot identification, Natural language processing, node classification, embedding techniques, twitter, graph neural networks, TwiBot22.
Abstract
Social media is a key resource in modern human communication as well as for information. Ease of access and global reach is a primary factor to the popularity of several social media platforms like Twitter. Social Media Bots are automated programs which are developed for social engagement. These bots, however, are being used with malicious intent as well, to spread fake news and manipulate the masses. Identification of social media bot accounts has become crucial since social media has become one of the primary sources of news and information for a lot of people. This project aims to propose Multirelation Bot Detection Graph Neural Network methods (MultiBotGNNs)to solve the bot detection as a node classification problem. We incorporate ideas from existing techniques for bot identification and use Natural Language Processing, and Deep Learning. This project is developing an approach to identify social media bots by using the user characteristics, their social media activities, the user account bio processed with NLP along with the social network graphs of different user relations, as follow, follower, and like. We experimented with the TwiBot22, which is a big dataset of bots. Our focus was to consider the structures of the relations for the prediction, and ignoring the tweet contents and show that we can good results without the extra process time for the tweets.
Recommended Citation
Jadhav, Ketan, "Multirelational Twitter Bot Detection using Graph Neural Networks" (2024). Master's Projects. 1394.
https://scholarworks.sjsu.edu/etd_projects/1394
