# Identification of Biased Terms in News Articles by Comparison of Outlet-specific Word Embeddings

arXiv: https://arxiv.org/abs/2112.07384 · HTML: https://mlsanity.com/paper/2112.07384/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 14 Dec 2021.

## Builds on (most cited first, 5 of 5)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Exploiting Similarities among Languages for Machine Translation](https://mlsanity.com/paper/1309.4168/) (2013)
- [The Geometry of Culture: Analyzing Meaning through Word Embeddings](https://mlsanity.com/paper/1803.09288/) (2018)
- [MBIC -- A Media Bias Annotation Dataset Including Annotator Characteristics](https://mlsanity.com/paper/2105.11910/) (2021)
- [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)

## Built on by (most cited first, 9 of 9)

- [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)
- [What company do words keep? Revisiting the distributional semantics of J.R. Firth & Zellig Harris](https://mlsanity.com/paper/2205.07750/) (2022)
- [An Interdisciplinary Approach for the Automated Detection and Visualization of Media Bias in News Articles](https://mlsanity.com/paper/2112.13352/) (2021)
- [Evolving linguistic divergence on polarizing social media](https://mlsanity.com/paper/2309.01659/) (2023)
- [Connecting the Dots in News Analysis: Bridging the Cross-Disciplinary Disparities in Media Bias and Framing](https://mlsanity.com/paper/2309.08069/) (2023)
- [Read Between the Lines: A Benchmark for Uncovering Political Bias in Bangla News Articles](https://mlsanity.com/paper/2510.03898/) (2025)
- [No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media](https://mlsanity.com/paper/2211.03634/) (2022)

## Abstract (arXiv)

Slanted news coverage, also called media bias, can heavily influence how news consumers interpret and react to the news. To automatically identify biased language, we present an exploratory approach that compares the context of related words. We train two word embedding models, one on texts of left-wing, the other on right-wing news outlets. Our hypothesis is that a word's representations in both word embedding spaces are more similar for non-biased words than biased words. The underlying idea is that the context of biased words in different news outlets varies more strongly than the one of non-biased words, since the perception of a word as being biased differs depending on its context. While we do not find statistical significance to accept the hypothesis, the results show the effectiveness of the approach. For example, after a linear mapping of both word embeddings spaces, 31% of the words with the largest distances potentially induce bias. To improve the results, we find that the dataset needs to be significantly larger, and we derive further methodology as future research direction. To our knowledge, this paper presents the first in-depth look at the context of bias words measured by word embeddings.