MGM : Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Media (2025)

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 therefore 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 which contains the dataset, code, and documentation.

## introduction the rise of the internet has offered many opportunities to publish information and to express opinions @xcite . concurrently, this easy means of distribution has accelerated the spread of misinformation and disinformation online which resembles news in form but lacks the journalistic standards that ensure its quality @xcite . @xcite has found that "fake news" spreads six times faster and reaches much farther than real news. any delay in profiling in rapidly evolving digital landscapes can lead to unchecked distribution of misleading content @xcite . profiling news outlets through nlp pipelines offers a proactive approach by enabling the early detection of potentially unreliable sources as soon as they publish content. since outlets with a history of biased or false information are more likely to do so again, profiling the media in advance allows us to quickly identify probable "fake news" by evaluating the reliability of the source itself. @xcite . early studies on automatic media profiling relied solely on text characteristics @xcite , which has proven particularly challenging. the complexity increases when the text features contain indeterminate noise, leading to classification errors @xcite . moreover, traditional methods struggle to capture the intricate relationships between entities, such as media outlets, content they publish, and audiences. @xcite constructed media graphs: nodes represent media, and edges represent audience overlap between media. they proposed a framework that captures both inherent and implicit information about media through interactive learning within the media ecosystem, addressing the limitations of relying solely on textual features. we analyze these media graphs and identify two key challenges: disconnected components and label sparsity. disconnected components prevent gnns from capturing long-range dependencies, limiting their ability to learn expressive node representations for classification tasks @xcite . prior studies @xcite address similar issues by using memory-based approaches that store global information throughout the graph using external memory modules. however, these methods require significant memory to store all node embeddings. to tackle these challenges, we present mgm, a novel method based on a variational expectation-maximization (em) framework that augments existing gnns to capture and exploit global information in media graphs. mgm seamlessly integrates local and global patterns, node features, and labels from globally similar nodes to enhance performance. unlike graph attention networks (gats) @xcite , which focus solely on local neighborhoods, mgm employs an external memory module to store precomputed node representations of all nodes. this approach not only reduces computational costs @xcite but also facilitates efficient node embedding retrieval. furthermore, mgm optimizes memory usage by focusing on a small set of candidate nodes, guided by a dirichlet prior distribution @xcite . the experimental results show that mgm substantially enhances the performance of baseline gnns, delivering a 10% increase across all evaluation measures on the media bias/fact check (mbfc) 2 data feature in the acl-2020 @xcite and the emnlp-2018 @xcite datasets. despite the lack of rich node features in the media graph, we enhance the dataset by scraping articles and wikipedia descriptions for acl-2020. pre-trained language models (plms) such as bert @xcite , roberta @xcite , distilbert @xcite , and debertav3 @xcite are fine-tuned to predict political bias and factuality. where textual data of media are inaccessible, mgm's representationbased probabilities compliment the gap. moreover, integrating mgm's probabilities with plms enhance the performance for both tasks. our contributions are as follows: ## methodology in this section, we present the problem formulation and provide a detailed description of the proposed framework, which leverages features, structural patterns, and label information from globally similar nodes to enhance gnns performance. furthermore, mgm integrates with plms to overcome the limitations of existing textual features to detect factuality and political bias. ## conclusion & future work our study focused on the underexplored problem of profiling news media in terms of factuality and political bias. to address the shortcomings of existing media graphs, we introduced mediagraph-mind (mgm), an innovative em framework that significantly enhances the performance of gnns by leveraging globally similar nodes. the external memory module of mgm efficiently stores and retrieves node representations, addressing the challenge of test-time inefficiency by selecting global similar nodes from a smaller candidate set based on a sparse node selection distribution. our experiments demonstrate that the integration of mgm features with plms consistently improves over existing baselines and establishes a new state-of-theart results. in future work, we plan to explore multi-graph fusion, multi-task learning, and ordinal classification for diverse graph structures in media profiling.