Inflating Topic Relevance with Ideology: A Case Study of Political Ideology Bias in Social Topic Detection Models (2020.coling-main)
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| Challenge: | a study examines the impact of political ideology biases in training data . topic detection methods may contain or propagate certain biase resulting in a skewed data collection . |
| Approach: | They propose to learn a text representation that is invariant to political ideology while still judging topic relevance. |
| Outcome: | The proposed model can be invariant to political ideology while still judging topic relevance. |
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We Can Detect Your Bias: Predicting the Political Ideology of News Articles (2020.emnlp-main)
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| Challenge: | a new study examines the role of media in predicting political ideology or bias in news articles . systematic exposure to bias in the news can foster intolerance and ideological segregation . |
| Approach: | They propose an adversarial media adaptation and a specially adapted triplet loss for predicting political ideology in news articles. |
| Outcome: | The proposed model improves over state-of-the-art models in this challenging setup. |
Multi-view Models for Political Ideology Detection of News Articles (D18-1)
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| Challenge: | Existing models for automatic detection of political ideology only leverage textual cues to identify the ideology evinced by a news article. |
| Approach: | They propose a novel attention based multi-view model that leverages cues from textual content and the network structure of news articles to identify political ideology. |
| Outcome: | The proposed model outperforms state of the art models by 10 percentage points on a battery of baselines and compares with baselines. |
Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020.acl-main)
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| Challenge: | 146 papers analyzing "bias" in NLP systems lack normative reasoning, we find . authors propose three recommendations for work analyzing “bias” in Nlp systems . |
| Approach: | They propose three recommendations for analyzing "bias" in NLP systems . they propose to focus on what kinds of system behaviors are harmful, in what ways, to whom, and why . |
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Bias in Opinion Summarisation from Pre-training to Adaptation: A Case Study in Political Bias (2024.eacl-long)
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| Challenge: | Existing studies have focused on extractive summarisation but limited attention has been paid to abstractive summaries. |
| Approach: | They propose to trace bias in abstractive summarisation models to social media opinions using different models and adaptation methods. |
| Outcome: | The proposed model is compared with other models and adaptation methods to summarise social media opinions using different models and adaption methods. |
How Susceptible are Large Language Models to Ideological Manipulation? (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have the potential to exert substantial influence on public perceptions and interactions with information. |
| Approach: | They examine how LLMs can learn and generalize ideological biases from their instruction-tuning data. |
| Outcome: | The LLMs show a startling ability to absorb ideology from one topic and generalize it to even unrelated ones. |
Probing Political Ideology in Large Language Models: How Latent Political Representations Generalize Across Tasks (2025.findings-emnlp)
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| Challenge: | Large language models encode rich internal representations of political ideology, but it remains unclear how these representations contribute to model decision-making. |
| Approach: | They apply inference-time interventions to steer a decoder-only transformer along learned ideological directions . they find that learned ideological representations generalize well to bias detection, but not as well to voting simulations . |
| Outcome: | The proposed model steers a transformer along learned ideological directions . political bias detection, voting preference simulation and bias neutralization are tested . |
POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection (2022.findings-naacl)
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| Challenge: | a lack of general-purpose tools to characterize and predict ideology across genres of text remains a challenge . a recent study compared ideology-driven pretraining tasks with long or formal written texts . |
| Approach: | They propose to use a large-scale dataset to train pretraining models that compare political news articles on the same story written by different ideologies. |
| Outcome: | The proposed model outperforms baseline models and state-of-the-art models on ideology prediction and stance detection tasks. |
Analyzing Political Bias in LLMs via Target-Oriented Sentiment Classification (2025.findings-acl)
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| Challenge: | Existing methods to analyze political biases rely on small-size intermediate tasks and the LLMs themselves. |
| Approach: | They propose an entropy-based inconsistency metric to encode political biases . they insert 1319 demographically and politically diverse politician names in 450 political sentences . |
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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. |
Towards Detecting Political Bias in Hindi News Articles (2022.acl-srw)
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| Challenge: | Political propaganda in recent times has been amplified by media news portals through biased reporting, creating untruthful narratives on serious issues . a dataset for this task was not available, therefore we developed a transformer-based transfer learning method to fine-tune the pre-trained network on our data. |
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