| Challenge: | Traditional NLP models have shown good performance in classifying media bias, but require careful model design and extensive tuning. |
| Approach: | They ask how well prompting of large language models can recognize media bias. |
| Outcome: | The prompt-based models deliver comparable performance to traditional models with greatly reduced effort and the availability of context substantially improves results. |
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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. |
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. |
In Plain Sight: Media Bias Through the Lens of Factual Reporting (D19-1)
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| Challenge: | lexical bias stems from content realization, or how things are said, but other forms of bias stem from content selection and organization. |
| Approach: | They use a dataset to analyze news articles annotated with 1,727 bias spans to investigate informational bias. |
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Media Bias, the Social Sciences, and NLP: Automating Frame Analyses to Identify Bias by Word Choice and Labeling (2020.acl-srw)
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| Challenge: | slanted news coverage can have negative effects on individuals and society . a system that helps readers to become aware of the differences in media coverage caused by bias is being developed. |
| Approach: | They propose to use natural language processing and deep learning to identify instances of WCL bias and estimate the frames they induce. |
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Measuring and Mitigating Media Outlet Name Bias in Large Language Models (2025.emnlp-main)
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| Challenge: | Existing studies have explored the potential political biases of large language models, but limited attention has been devoted to the effects of media outlet names. |
| Approach: | They propose to quantify media outlet name biases in large language models and leverage this metric to develop an automated prompt optimization framework. |
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Neural Media Bias Detection Using Distant Supervision With BABE - Bias Annotations By Experts (2021.findings-emnlp)
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| Challenge: | Existing studies on the detection and aggregation of media bias lack a gold standard data set and high context dependencies. |
| Approach: | They propose to use a data set to identify media bias by word and sentence level . they propose to train a model to detect bias-inducing sentences in news articles automatically . |
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Adapting Bias Evaluation to Domain Contexts using Generative Models (2025.emnlp-main)
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| Challenge: | Existing approaches to assess social bias in NLP systems face limitations in scalability and fidelity across domains. |
| Approach: | They propose a domain-adaptive framework that uses prompting with Large Language Models to automatically transform template-based bias datasets into domain-specific variants. |
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Profiling News Media for Factuality and Bias Using LLMs and the Fact-Checking Methodology of Human Experts (2025.findings-acl)
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| Challenge: | Important efforts to characterize news media outlets in terms of their political bias and factuality are labor-intensive and prone to human biases. |
| Approach: | They propose a method that emulates criteria used by professional fact-checkers to assess the factuality and political bias of an entire outlet. |
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Context in Informational Bias Detection (2020.coling-main)
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| Challenge: | Informational bias is conveyed through sentences or clauses that provide tangential, speculative or background information that can sway readers’ opinions towards entities. |
| Approach: | They explore four kinds of context for informational bias in English news articles . integrating event context improves classification performance over a strong baseline . |
| Outcome: | The best-performing model outperforms the baseline on longer sentences and sentences from politically centrist articles. |
Mind Your Bias: A Critical Review of Bias Detection Methods for Contextual Language Models (2022.findings-emnlp)
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| Challenge: | Existing methods for detection of biases in contextual language models are inconsistent and inconclusive. |
| Approach: | They propose to use word embedding association test to detect biases in contextual language models to compare them with other methods. |
| Outcome: | The proposed methods are inconsistent and inconclusive for language models with word embeddings. |