Papers with media bias detection
IndiVec: An Exploration of Leveraging Large Language Models for Media Bias Detection with Fine-Grained Bias Indicators (2024.findings-eacl)
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| Challenge: | Existing studies on social media bias detection focus on fine-tuning models specific to particular datasets and testing them on corresponding test sets. |
| Approach: | They propose a general bias detection framework, IndiVec, built upon large language models and vector databases. |
| Outcome: | The proposed framework outperforms baseline methods on four political bias datasets and provides explicit top-k indicators to interpret bias predictions. |
The Promises and Pitfalls of LLM Annotations in Dataset Labeling: a Case Study on Media Bias Detection (2025.findings-naacl)
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Tomáš Horych, Christoph Mandl, Terry Ruas, Andre Greiner-Petter, Bela Gipp, Akiko Aizawa, Timo Spinde
| Challenge: | Recent research suggests using Large Language Models (LLMs) to automate the annotation process, reducing these costs while maintaining data quality. |
| Approach: | They propose to use Large Language Models to automate annotation process and train classifiers on large datasets. |
| Outcome: | The proposed model outperforms all of the annotator LLMs on two media bias benchmark datasets (BABE and BASIL) while maintaining data quality. |
Investigating Bias in LLM-Based Bias Detection: Disparities between LLMs and Human Perception (2025.coling-main)
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| Challenge: | Detecting media bias is critical due to the spread of misinformation and disinformation on social media platforms. |
| Approach: | They investigate the presence and nature of bias within large language models and its consequential impact on media bias detection. |
| Outcome: | The proposed debiasing strategies include prompt engineering and model fine-tuning. |
MAGPIE: Multi-Task Analysis of Media-Bias Generalization with Pre-Trained Identification of Expressions (2024.lrec-main)
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Tomáš Horych, Martin Paul Wessel, Jan Philip Wahle, Terry Ruas, Jerome Waßmuth, André Greiner-Petter, Akiko Aizawa, Bela Gipp, Timo Spinde
| Challenge: | Existing approaches to media bias detection lack generalizability, resulting in limited generalizarability. |
| Approach: | They propose a large-scale multi-task pre-training approach specifically tailored for media bias detection that can be used to train 59 bias-related tasks. |
| Outcome: | The proposed approach outperforms existing methods on the BABE dataset with a relative improvement of 3.3% F1-score. |
Multi-document Summarization through Multi-document Event Relation Graph Reasoning in LLMs: a case study in Framing Bias Mitigation (2025.acl-long)
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| Challenge: | a recent study has focused on detecting media bias in news articles . a multi-document event relation graph is used to generate a neutralized summary . |
| Approach: | They propose to generate a neutralized summary given multiple articles presenting different ideological views. |
| Outcome: | The proposed method mitigates media bias and improves content preservation. |