Summary of Mgm: Global Understanding Of Audience Overlap Graphs For Predicting the Factuality and the Bias Of News Media, by Muhammad Arslan Manzoor et al.
MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Media
by Muhammad Arslan Manzoor, Ruihong Zeng, Dilshod Azizov, Preslav Nakov, Shangsong Liang
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed MediaGraphMind (MGM) framework is an innovative solution for profiling news media from a political bias and factuality perspective. Building upon Pre-trained Language Models (PLMs) and Graph Neural Networks (GNNs), MGM addresses the limitations of these traditional methods by leveraging global node features, structural patterns, and label information within a variational Expectation-Maximization (EM) framework. This approach enables GNNs to capture long-range dependencies for learning expressive node representations and enhances PLMs by integrating structural information, leading to improved performance. The framework demonstrates state-of-the-art results in extensive experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to figure out which news sources are trustworthy or biased towards a particular party. This paper helps develop a way to do just that! They created a new method called MediaGraphMind (MGM) that combines ideas from two other approaches: language models and graph networks. The problem with these methods is they don’t look at the big picture, missing important connections between news sources. MGM fixes this by considering not only what each source says but also how it relates to others. This new approach shows great results and could help you make more informed decisions when searching for reliable information online. |
