CE-BERT: Concise and Efficient BERT-Based Model for Detecting Rumors on Twitter

Abstract

Detecting rumours on social media requires careful consideration of content and context. Graph-based neural network techniques have been used to explore the contextual features of tweets. However, reliable contextual feature extraction from Twitter is challenging due to its rules and restrictions. BERT-based models extract features directly from tweet content but can be computationally expensive, limiting their practicality. We propose CE-BERT, a concise and efficient model to detect rumours on Twitter using only source text. By reducing the number of BERT parameters, we improved processing speed without sacrificing performance. Our experiments show that CE-BERT outperformed BERT textsubscript BASE and RoBERTa, achieving comparable results to leading graph-based models. CE-BERT is more promising for real-world scenarios due to Twitter’s nature. Our results indicate that CE-BERT is faster, more concise, and more efficient than other advanced models. We hope our research aids in developing practical and effective techniques for detecting rumours on social media.

Keywords

• CE-BERT
• rumour detection
• text processing
• transformer models
• Twitter