# Neural Based Statement Classification for Biased Language

arXiv: https://arxiv.org/abs/1811.05740 · HTML: https://mlsanity.com/paper/1811.05740/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2018.

## Built on by (most cited first, 17 of 17)

- [Neural Media Bias Detection Using Distant Supervision With BABE -- Bias Annotations By Experts](https://mlsanity.com/paper/2209.14557/) (2022)
- [MBIC -- A Media Bias Annotation Dataset Including Annotator Characteristics](https://mlsanity.com/paper/2105.11910/) (2021)
- [Introducing MBIB -- the first Media Bias Identification Benchmark Task and Dataset Collection](https://mlsanity.com/paper/2304.13148/) (2023)
- [Exploiting Transformer-based Multitask Learning for the Detection of Media Bias in News Articles](https://mlsanity.com/paper/2211.03491/) (2022)
- [Towards A Reliable Ground-Truth For Biased Language Detection](https://mlsanity.com/paper/2112.07421/) (2021)
- [RECAST: Enabling User Recourse and Interpretability of Toxicity Detection Models with Interactive Visualization](https://mlsanity.com/paper/2102.04427/) (2021)
- [An Interdisciplinary Approach for the Automated Detection and Visualization of Media Bias in News Articles](https://mlsanity.com/paper/2112.13352/) (2021)
- [A Corpus for Sentence-level Subjectivity Detection on English News Articles](https://mlsanity.com/paper/2305.18034/) (2023)
- [A Survey on Semantic Processing Techniques](https://mlsanity.com/paper/2310.18345/) (2023)
- [MAGPIE: Multi-Task Media-Bias Analysis Generalization for Pre-Trained Identification of Expressions](https://mlsanity.com/paper/2403.07910/) (2024)
- [CO-STAR: Conceptualisation of Stereotypes for Analysis and Reasoning](https://mlsanity.com/paper/2112.00819/) (2021)
- [WikiSQE: A Large-Scale Dataset for Sentence Quality Estimation in Wikipedia](https://mlsanity.com/paper/2305.05928/) (2023)
- [Target-Aware Contextual Political Bias Detection in News](https://mlsanity.com/paper/2310.01138/) (2023)
- [Ideology-Based LLMs for Content Moderation](https://mlsanity.com/paper/2510.25805/) (2025)
- [KUCST at CheckThat 2023: How good can we be with a generic model?](https://mlsanity.com/paper/2306.09108/) (2023)
- [Epistemological Bias As a Means for the Automated Detection of Injustices in Text](https://mlsanity.com/paper/2407.06098/) (2024)
- [MABPD: Multi-Agent Bias Probing & Detection via Structured Argument Debate](https://mlsanity.com/paper/2609.04841/) (2026)

## Abstract (arXiv)

Biased language commonly occurs around topics which are of controversial nature, thus, stirring disagreement between the different involved parties of a discussion. This is due to the fact that for language and its use, specifically, the understanding and use of phrases, the stances are cohesive within the particular groups. However, such cohesiveness does not hold across groups. In collaborative environments or environments where impartial language is desired (e.g. Wikipedia, news media), statements and the language therein should represent equally the involved parties and be neutrally phrased. Biased language is introduced through the presence of inflammatory words or phrases, or statements that may be incorrect or one-sided, thus violating such consensus. In this work, we focus on the specific case of phrasing bias, which may be introduced through specific inflammatory words or phrases in a statement. For this purpose, we propose an approach that relies on a recurrent neural networks in order to capture the inter-dependencies between words in a phrase that introduced bias. We perform a thorough experimental evaluation, where we show the advantages of a neural based approach over competitors that rely on word lexicons and other hand-crafted features in detecting biased language. We are able to distinguish biased statements with a precision of P=0.92, thus significantly outperforming baseline models with an improvement of over 30%. Finally, we release the largest corpus of statements annotated for biased language.