Papers by Xinliang Zhang
All Things Considered: Detecting Partisan Events from News Media with Cross-Article Comparison (2023.emnlp-main)
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| Challenge: | a recent study shows that media influence opinion via the inclusion or omission of partisan events. |
| Approach: | They develop a latent variable-based framework to predict the ideology of news articles by comparing multiple articles on the same story and identifying partisan events whose inclusion or omission reveals ideology. |
| Outcome: | The proposed framework validates the existence of partisan event selection and detects partisan events and article ideology better than baselines. |
Crossing the Aisle: Unveiling Partisan and Counter-Partisan Events in News Reporting (2023.findings-emnlp)
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| Challenge: | Prior work in NLP has only studied media bias via linguistic style and word usage. |
| Approach: | They annotate a dataset containing 8,511 (counter-)partisan event annotations in 304 news articles from ideologically diverse media outlets. |
| Outcome: | The proposed dataset contains 8,511 (counter-)partisan event annotations in 304 news articles from ideologically diverse media outlets. |
You Are What You Annotate: Towards Better Models through Annotator Representations (2023.findings-emnlp)
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| Challenge: | Annotator disagreement is ubiquitous in natural language processing tasks. |
| Approach: | They propose to model annotators' idiosyncrasies and account for their idioms by creating representations for each annotator and their annotations. |
| Outcome: | The proposed model improves on an existing dataset with eight annotators with inherent disagreements while increasing model size by 1%. |