# An Interdisciplinary Approach for the Automated Detection and Visualization of Media Bias in News Articles

arXiv: https://arxiv.org/abs/2112.13352 · HTML: https://mlsanity.com/paper/2112.13352/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 57 references that are in the map.
- First posted on arXiv 26 Dec 2021.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://mlsanity.com/paper/2005.04118/) (2020)
- [A Multiscale Visualization of Attention in the Transformer Model](https://mlsanity.com/paper/1906.05714/) (2019)
- [In Plain Sight: Media Bias Through the Lens of Factual Reporting](https://mlsanity.com/paper/1909.02670/) (2019)
- [Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity](https://mlsanity.com/paper/2010.10652/) (2020)
- [MBIC -- A Media Bias Annotation Dataset Including Annotator Characteristics](https://mlsanity.com/paper/2105.11910/) (2021)
- [Neural Based Statement Classification for Biased Language](https://mlsanity.com/paper/1811.05740/) (2018)
- [Enabling News Consumers to View and Understand Biased News Coverage: A Study on the Perception and Visualization of Media Bias](https://mlsanity.com/paper/2105.09640/) (2021)
- [Do You Think It's Biased? How To Ask For The Perception Of Media Bias](https://mlsanity.com/paper/2112.07392/) (2021)
- [Towards A Reliable Ground-Truth For Biased Language Detection](https://mlsanity.com/paper/2112.07421/) (2021)
- [Identification of Biased Terms in News Articles by Comparison of Outlet-specific Word Embeddings](https://mlsanity.com/paper/2112.07384/) (2021)
- [TASSY -- A Text Annotation Survey System](https://mlsanity.com/paper/2112.07391/) (2021)
- [Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification](https://mlsanity.com/paper/1703.02504/) (2017)

## Built on by (most cited first, 6 of 6)

- [Introducing MBIB -- the first Media Bias Identification Benchmark Task and Dataset Collection](https://mlsanity.com/paper/2304.13148/) (2023)
- [A Domain-adaptive Pre-training Approach for Language Bias Detection in News](https://mlsanity.com/paper/2205.10773/) (2022)
- [The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias](https://mlsanity.com/paper/2312.16148/) (2023)
- [Exploiting Transformer-based Multitask Learning for the Detection of Media Bias in News Articles](https://mlsanity.com/paper/2211.03491/) (2022)
- [Leveraging Large Language Models for Automated Definition Extraction with TaxoMatic A Case Study on Media Bias](https://mlsanity.com/paper/2504.00343/) (2025)
- [A Commonsense-Infused Language-Agnostic Learning Framework for Enhancing Prediction of Political Polarity in Multilingual News Headlines](https://mlsanity.com/paper/2212.00298/) (2022)

## Abstract (arXiv)

Media coverage has a substantial effect on the public perception of events. Nevertheless, media outlets are often biased. One way to bias news articles is by altering the word choice. The automatic identification of bias by word choice is challenging, primarily due to the lack of gold-standard data sets and high context dependencies. In this research project, I aim to devise data sets and methods to identify media bias. To achieve this, I plan to research methods using natural language processing and deep learning while employing models and using analysis concepts from psychology and linguistics. The first results indicate the effectiveness of an interdisciplinary research approach. My vision is to devise a system that helps news readers become aware of media coverage differences caused by bias. So far, my best performing BERT-based model is pre-trained on a larger corpus consisting of distant labels, indicating that distant supervision has the potential to become a solution for the difficult task of bias detection.