Research on Tibetan Rumor Detection Method Based on Word2Vec-BiLSTM-Att

With the rapid development of network technology, the Tibetan cyberspace is facing the severe challenge of rumor diffusion. In response to the deficiencies of existing Tibetan pre-trained models in learning social media expression patterns, a rumor detection method based on Word2Vec-BiLSTM-Att is proposed. This method first pre-trains the Word2Vec model with social media expression patterns, then uses BiLSTM for feature extraction, and combines a self-attention mechanism to enhance the model's focus on valid comment information, thereby improving the accuracy of Tibetan rumor detection. Experimental results show that this method has improved the accuracy by 2.4% and 0.2% compared to language models like CINO and TiBERT, which are trained with Tibetan news data, on a self-built Tibetan rumor dataset.