Twitter Bot Detection Using Relational Graph Convolutional Networks and Convolutional Neural Networks
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Abstract
The increasing fake number accounts and bots on Twitter threaten cyber security, information integrity, and public discourse. modern methods to define and block bots', primarily depend on user information metadata, content, and behavioral heuristics, have confirmed inactive against more developed bots that simulate actual user behavior. Graph Neural Networks (GNNs) can help. User interaction and Social media can be modeled as a graph, where users are represented as nodes and their interactions as edges, and GNNs can mesh individual and relational data into a single entity. This paper analyzes these accounts from two various methodological viewpoints looked at Conventional Neural Networks (CNNs) and Relational Graph Convolutional Networks (R-GCNs). While convolutional neural network (CNN) models have shown high efficiency in recognizing linguistic indicators and metadata, their reliance on surface features has limited their ability to handle computations designed to accurately simulate human texts and metadata. The R-GCN model architecture is the most outstanding, outperforming all models particularly in terms of F1 and ROC-AUC, by successfully capturing different relationship patterns—follow, rewet and mention. this paper highlights the quality and limitations of current detection schemes and proposes modern methods for automatic interpretation, multimedia integration, and cross-platform adaptation.
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