Multimodal Detection of Bots on X (Twitter) Using Transformers

Ilias, Loukas; Michail Kazelidis, Ioannis; Askounis, Dimitris

doi:10.1109/tifs.2024.3435138

IEEE Trans.Inform.Forensic Secur.

2024

DOI: 10.1109/tifs.2024.3435138

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Multimodal Detection of Bots on X (Twitter) Using Transformers

Loukas Ilias,

Ioannis Michail Kazelidis,

Dimitris Askounis

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2024

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Cited by 2 publications

References 60 publications

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Cybersecurity in social networks: An ensemble model for Twitter bot detection

Darem,

Alhashmi,

Alanazi

et al. 2024

Int. j. adv. appl. sci.

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The increasing presence of bot accounts on social media platforms creates major challenges for ensuring truthful and reliable online communication. This study examines how well ensemble learning techniques can identify bot accounts on Twitter. Using a dataset from Kaggle, which provides detailed information about accounts and labels them as either bot or human, we applied and tested several machine learning methods, including logistic regression, decision trees, random forests, XGBoost, support vector machines, and multi-layer perceptrons. The ensemble model, which merges predictions from individual classifiers, achieved the best performance, with 90.22% accuracy and a precision rate of 92.39%, showing strong detection capability with few false positives. Our results emphasize the potential of ensemble learning to improve bot detection by combining the strengths of different classifiers. The study highlights the need for reliable and understandable detection systems to preserve the authenticity of social media, addressing the changing tactics used by bot developers. Future research should explore additional types of data and ways to make models easier to understand, aiming to further improve detection results.

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Cybersecurity in social networks: An ensemble model for Twitter bot detection

Darem,

Alhashmi,

Alanazi

et al. 2024

Int. j. adv. appl. sci.

View full text Add to dashboard Cite

show abstract

A graph neural architecture search approach for identifying bots in social media

Tzoumanekas,

Chatzianastasis,

Ilias

et al. 2024

Front. Artif. Intell.

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Social media platforms, including X, Facebook, and Instagram, host millions of daily users, giving rise to bots automated programs disseminating misinformation and ideologies with tangible real-world consequences. While bot detection in platform X has been the area of many deep learning models with adequate results, most approaches neglect the graph structure of social media relationships and often rely on hand-engineered architectures. Our work introduces the implementation of a Neural Architecture Search (NAS) technique, namely Deep and Flexible Graph Neural Architecture Search (DFG-NAS), tailored to Relational Graph Convolutional Neural Networks (RGCNs) in the task of bot detection in platform X. Our model constructs a graph that incorporates both the user relationships and their metadata. Then, DFG-NAS is adapted to automatically search for the optimal configuration of Propagation and Transformation functions in the RGCNs. Our experiments are conducted on the TwiBot-20 dataset, constructing a graph with 229,580 nodes and 227,979 edges. We study the five architectures with the highest performance during the search and achieve an accuracy of 85.7%, surpassing state-of-the-art models. Our approach not only addresses the bot detection challenge but also advocates for the broader implementation of NAS models in neural network design automation.

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Multimodal Detection of Bots on X (Twitter) Using Transformers

Cited by 2 publications

References 60 publications

Cybersecurity in social networks: An ensemble model for Twitter bot detection

Cybersecurity in social networks: An ensemble model for Twitter bot detection

A graph neural architecture search approach for identifying bots in social media

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