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

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

Blog Article

Detecting rumours on social media requires careful Shorts 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 Travel - Roof Racks advanced models.We hope our research aids in developing practical and effective techniques for detecting rumours on social media.