| 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. |
| Approach: | They propose a transformer-based transfer learning method to fine-tune the pre-trained network on the data for this bias detection. |
| Outcome: | The proposed method fine-tunes the pre-trained network on the data to detect political bias in Hindi news articles. |
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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. |
Discovering Biased News Articles Leveraging Multiple Human Annotations (2020.lrec-1)
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| 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. |
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. |
Annotating and Analyzing Biased Sentences in News Articles using Crowdsourcing (2020.lrec-1)
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| 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. |
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. |
| Outcome: | The proposed model shows that informational bias appears more frequently than lexical bias. |
Quantifying Generative Media Bias with a Corpus of Real-world and Generated News Articles (2024.findings-emnlp)
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| 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. |
NSIT@NLP4IF-2019: Propaganda Detection from News Articles using Transfer Learning (D19-50)
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| Challenge: | In this paper, we describe our approach and system description for NLP4IF 2019 Workshop: Shared Task on Fine-Grained Propaganda Detection. |
| Approach: | They propose to use document Embeddings and LSTM to detect whether a sentence contains a propagandistic agenda. |
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CLoSE: Contrastive Learning of Subframe Embeddings for Political Bias Classification of News Media (2022.coling-1)
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| Challenge: | Framing is a political strategy in which journalists and politicians emphasize certain aspects of an issue to influence and sway public opinion. |
| Approach: | They propose a BERT-based model which embeds indicators of frames from news articles in order to predict political bias. |
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Come hither or go away? Recognising pre-electoral coalition signals in the news (2021.emnlp-main)
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Ines Rehbein, Simone Paolo Ponzetto, Anna Adendorf, Oke Bahnsen, Lukas Stoetzer, Heiner Stuckenschmidt
| Challenge: | In this paper, we decompose the task of recognizing from the news coverage leading up to an election the (un)willingness of political parties to form a coalition into two related, but distinct tasks. |
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| Outcome: | The proposed approach improves over a strong monolingual transfer learning baseline. |
Disentangling Structure and Style: Political Bias Detection in News by Inducing Document Hierarchy (2023.findings-emnlp)
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| 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. |