A News Media Bias and Factuality Profiling Framework Assisted by Modeling Correlation

News media profiling is helpful in preventing the spread of fake news at the source and maintaining a good media and news ecosystem. Most previous works only extract features and evaluate media from one dimension independently, ignoring the interconnections between different aspects. This paper proposes a novel news media bias and factuality profiling framework assisted by correlated features. This framework models the relationship and interaction between media bias and factuality, utilizing this relationship to assist in the prediction of profiling results. Our approach extracts features independently while aligning and fusing them through recursive convolution and attention mechanisms, thus harnessing multi-scale interactive information across different dimensions and levels. This method improves the effectiveness of news media evaluation. Experimental results indicate that our proposed framework significantly outperforms existing methods, achieving the best performance in Accuracy and F1 score, improving by at least 1% compared to other methods. This paper further analyzes and discusses based on the experimental results.