This project aims to build computational systems to detect and quantify how media ideology affects the creation and presentation of news at the level of articles and their constituent events. This project will promote the transparency of news production and enhance public awareness of media decisions. The developed tools can effectively and efficiently support the measurement of media ideology at organization- and article-levels, which facilitates research in broad areas, including political science, social science, and communications. The proposed research will involve graduate and undergraduate students from a diverse array of backgrounds, especially underrepresented groups. The developed datasets and methods will form the basis of modules in newly developed courses. The knowledge produced in the project will be distributed to the public via demos, published blogs, talks at podcasts, and guest essays to newspapers.
Media bias, polarization, entity extraction, event extraction, ideology measurement
NSF, Award Number: 2127747. Duration: October 1, 2021 - September 30, 2024.
Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentation. Xinliang Frederick Zhang, Nick Beauchamp, and Lu Wang. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022.
Late Fusion with Triplet Margin Objective for Multimodal Ideology Prediction and Analysis. Changyuan Qiu, Winston Wu, Xinliang Frederick Zhang, and Lu Wang. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022.
POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection. Yujian Liu, Xinliang Frederick Zhang, David Wegsman, Nick Beauchamp, and Lu Wang. Findings of the Conference of the North American Chapter of the Association for Computational Linguistics (Findings of NAACL), 2022.