A Model for Detecting Fake News by Integrating Domain-Specific Emotional and Semantic Features

With the rapid spread of Internet information and the spread of fake news,the detection of fake news becomes more and more important.Traditional detection methods often rely on a single emotional or semantic feature to identify fake news,but these methods have limitations when dealing with news in specific domains.In order to solve the problem of weak feature correlation between data from different domains,a model for detecting fake news by integrating domain-specific emotional and semantic features is proposed.This method makes full use of the attention mechanism,grasps the correlation between different features,and effectively improves the effect of feature fusion.The algorithm first extracts the semantic features of news text through the Bi-LSTM(Bidirectional Long Short-Term Memory)layer to capture the contextual relevance of news text.Senta-BiLSTM is then used to extract emotional features and predict the probability of positive and negative emotions in the text.It then uses domain features as an enhancement feature and attention mechanism to fully capture more fine-grained emotional features associated with that domain.Finally,the fusion features are taken as the input of the fake news detection classifier,combined with the multi-task representation of information,and the MLP and Softmax functions are used for classification.The experimental results show that on the Chinese dataset Weibo21,the F1 value of this model is 0.958,4.9% higher than that of the sub-optimal model;on the English dataset FakeNewsNet,the F1 value of the detection result of this model is 0.845,1.8% higher than that of the sub-optimal model,which is advanced and feasible.

Cross-Target Stance Detection with Sentiments-Aware Hierarchical Attention Network

The task of cross-target stance detection faces significant challenges due to the lack of additional background information in emerging knowledge domains and the colloquial nature of language patterns.Traditional stance detection methods often struggle with understanding limited context and have insufficient generalization across diverse sentiments and semantic structures.This paper focuses on effectively mining and utilizing sentimentsemantics knowledge for stance knowledge transfer and proposes a sentiment-aware hierarchical attention network(SentiHAN)for cross-target stance detection.SentiHAN introduces an improved hierarchical attention network designed to maximize the use of high-level representations of targets and texts at various fine-grain levels.This model integrates phrase-level combinatorial sentiment knowledge to effectively bridge the knowledge gap between known and unknown targets.By doing so,it enables a comprehensive understanding of stance representations for unknown targets across different sentiments and semantic structures.The model’s ability to leverage sentimentsemantics knowledge enhances its performance in detecting stances that may not be directly observable from the immediate context.Extensive experimental results indicate that SentiHAN significantly outperforms existing benchmark methods in terms of both accuracy and robustness.Moreover,the paper employs ablation studies and visualization techniques to explore the intricate relationship between sentiment and stance.These analyses further confirm the effectiveness of sentence-level combinatorial sentiment knowledge in improving stance detection capabilities.