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Detection of Twitter bot accounts using Deep Learning Techniques

Chintakayala, Rupesh Sai Baba (2023) Detection of Twitter bot accounts using Deep Learning Techniques. Masters thesis, Dublin, National College of Ireland.

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Automation has become a core part of everyday work, especially on social media as posting daily updates related to news, sports, and current affairs. Using automated programs known as bots to post content on social media on behalf of humans is commonly used across all major Twitter accounts. This advancement gave rise to a new set of bots that can mimic human behavior, and act as a community making it difficult to differentiate genuine human accounts from bot accounts. The aim of this project is to use the state-of-the-art graph-based Neural Network models on the most comprehensive and largest Twitter bot detection dataset (Twibot-20) to date and use the pre-trained model RoBERTa for encoding the Twitter data and develop a deep learning model to detect bots Twitter. Extensive experiments were conducted using all the models developed and the RGCN (Relational Graph Convolutional Neural Networks) model performed better than all the other models with an accuracy of 85.8%, an f1-score of 87.21%, and an MCC of 71.39%.

Item Type: Thesis (Masters)
Anant, Aaloka
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources > The Internet > World Wide Web > Websites > Online social networks
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > The Internet > World Wide Web > Websites > Online social networks
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 17 May 2023 16:14
Last Modified: 17 May 2023 16:14

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