Open Access

ARTICLE

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

by Qi Wang¹, Chenxin Li^1,*, Chichen Lin², Weijian Fan³, Shuang Feng¹, Yuanzhong Wang⁴

1 School of Computer and Cyber Sciences, Communication University of China, Beijing, 100024, China
2 State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, 100024, China
3 School of Data Science and Intelligent Media, Communication University of China, Beijing, 100024, China
4 Beijing 797 Audio Co., Ltd., Beijing, 100016, China

* Corresponding Author: Chenxin Li. Email:

Computers, Materials & Continua 2024, 81(2), 3351-3369. https://doi.org/10.32604/cmc.2024.057191

Received 10 August 2024; Accepted 12 October 2024; Issue published 18 November 2024

Abstract

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.

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Keywords

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