MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Media
Manzoor, Muhammad Arslan Zeng, Ruihong Azizov, Dilshod Nakov, Preslav Liang, Shangsong
Manzoor, Muhammad Arslan
Zeng, Ruihong
Azizov, Dilshod
Nakov, Preslav
Liang, Shangsong
Supervisor
Department
Natural Language Processing
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
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Abstract
In the current era of rapidly growing digital data, evaluating the political bias and factuality of news outlets has become more important for seeking reliable information online. In this work, we study the classification problem of profiling news media from the lens of political bias and factuality. Traditional profiling methods, such as pre-trained language models (PLMs) and graph neural networks (GNNs) have shown promising results, but they face notable challenges. PLMs focus solely on textual features, causing them to overlook the complex relationships between entities, while GNNs often struggle with media graphs containing disconnected components and insufficient labels. To address these limitations, we propose MediaGraphMind (MGM), an effective solution within a variational Expectation-Maximization (EM) framework. Instead of relying on limited neighboring nodes, MGM leverages features, structural patterns, and label information from globally similar nodes. Such a framework not only enables GNNs to capture long-range dependencies for learning expressive node representations, but also enhances PLMs by integrating structural information and thus improving the performance of both models. The extensive experiments demonstrate the effectiveness of the proposed framework and achieve new state-of-the-art results. Further, we share our repository(1) which contains the dataset, code, and documentation.
Citation
M. A. Manzoor, R. Zeng, D. Azizov, P. Nakov, and S. Liang, “MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Media,” vol. 1, pp. 7279–7295, Jun. 2025, doi: 10.18653/V1/2025.NAACL-LONG.373
Source
Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Conference
2025 Conference of the North American Chapter of the Association for Computational Linguistics-NAACL
Keywords
News Media Profiling, Graph Neural Networks, Audience Overlap Graphs, Political Bias Detection, Factuality Prediction, Variational Expectation-Maximization, Pre-trained Language Models, Media Bias/Fact Check Dataset
Subjects
Source
2025 Conference of the North American Chapter of the Association for Computational Linguistics-NAACL
Publisher
Association for Computational Linguistics