Disentangling Structure and Style: Political Bias Detection in News by Inducing Document Hierarchy (2023.findings-emnlp)
Copied to clipboard
| Challenge: | a new method to detect political bias in news articles overcomes this domain dependency . partisan bias exists in various social issues, including the 2016 presidential election . |
| Approach: | They propose a multi-head hierarchical attention model that encodes the structure of long documents through a diverse ensemble of attention heads. |
| Outcome: | The proposed model outperforms existing methods for detecting political bias in news articles. |
Similar Papers
An Integrated Approach for Political Bias Prediction and Explanation Based on Discursive Structure (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for predicting and explaining political biases rely on lexical cues. |
| Approach: | They propose an approach to automatically characterize biases that takes into account structural differences and is efficient for long texts. |
| Outcome: | The proposed approach is efficient for long texts and takes into account structural differences. |
Sentence-level Media Bias Analysis Informed by Discourse Structures (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent work on detecting media bias at the level of individual articles is limited to single sentences. |
| Approach: | They propose to use a news discourse structure and PDTB discourse relations to identify bias sentences within an article that can illuminate and explain the overall bias of the entire article. |
| Outcome: | The proposed model can detect bias at the level of individual articles and a single sentence can explain it. |
We Can Detect Your Bias: Predicting the Political Ideology of News Articles (2020.emnlp-main)
Copied to clipboard
| 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)
Copied to clipboard
| 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. |
All Things Considered: Detecting Partisan Events from News Media with Cross-Article Comparison (2023.emnlp-main)
Copied to clipboard
| 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. |
Discovering Biased News Articles Leveraging Multiple Human Annotations (2020.lrec-1)
Copied to clipboard
| Challenge: | Political propaganda and one-sided views can be found in the news and can cause distrust in media. |
| Approach: | They propose to annotate politically biased news articles by an algorithm annotated by domain experts and crowd workers and to compare them to crowd workers. |
| Outcome: | The proposed method compares domain experts to crowd workers and shows that bias can be detected automatically. |
Annotating and Analyzing Biased Sentences in News Articles using Crowdsourcing (2020.lrec-1)
Copied to clipboard
| Challenge: | a lack of publicly available news bias datasets has hindered efforts to detect subtle biases in news articles. |
| Approach: | They propose a news bias dataset which contains sentences with bias labels . they propose to use the dataset to develop and evaluate methods for detecting news bias . |
| Outcome: | The proposed dataset can be used for analyzing news bias and for developing and evaluating methods for news bias detection. |
Profiling News Media for Factuality and Bias Using LLMs and the Fact-Checking Methodology of Human Experts (2025.findings-acl)
Copied to clipboard
| 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. |
| Outcome: | The proposed method improves on baselines and with multiple LLMs. |
Quantifying Generative Media Bias with a Corpus of Real-world and Generated News Articles (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies focus on LLMs undertaking political questionnaires, which offers only limited insights into their biases and operational nuances. |
| Approach: | They propose to use a curated dataset to generate 56,700 synthetic articles using nine LLMs. |
| Outcome: | The proposed model can detect political biases using supervised models and LLMs. |
WIKIBIAS: Detecting Multi-Span Subjective Biases in Language (2021.findings-emnlp)
Copied to clipboard
| Challenge: | a particular type of bias is subjective bias, which introduces improper attitudes or presents a statement with the presupposition of truth. |
| Approach: | They propose to annotate a Wikipedia edits corpus with 4,000 sentence pairs to detect subjective bias. |
| Outcome: | The proposed dataset can be used as a research benchmark and generalize to multiple domains. |