# MBIC -- A Media Bias Annotation Dataset Including Annotator Characteristics

arXiv: https://arxiv.org/abs/2105.11910 · HTML: https://mlsanity.com/paper/2105.11910/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 12 references that are in the map.
- First posted on arXiv 20 May 2021.

## Builds on (most cited first, 2 of 2)

- [In Plain Sight: Media Bias Through the Lens of Factual Reporting](https://mlsanity.com/paper/1909.02670/) (2019)
- [Neural Based Statement Classification for Biased Language](https://mlsanity.com/paper/1811.05740/) (2018)

## Built on by (most cited first, 24 of 24)

- [Neural Media Bias Detection Using Distant Supervision With BABE -- Bias Annotations By Experts](https://mlsanity.com/paper/2209.14557/) (2022)
- [A Domain-adaptive Pre-training Approach for Language Bias Detection in News](https://mlsanity.com/paper/2205.10773/) (2022)
- [The Promises and Pitfalls of LLM Annotations in Dataset Labeling: a Case Study on Media Bias Detection](https://mlsanity.com/paper/2411.11081/) (2024)
- [Do You Think It's Biased? How To Ask For The Perception Of Media Bias](https://mlsanity.com/paper/2112.07392/) (2021)
- [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)
- [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)
- [Voices in a Crowd: Searching for Clusters of Unique Perspectives](https://mlsanity.com/paper/2407.14259/) (2024)
- [An Interdisciplinary Approach for the Automated Detection and Visualization of Media Bias in News Articles](https://mlsanity.com/paper/2112.13352/) (2021)
- [TASSY -- A Text Annotation Survey System](https://mlsanity.com/paper/2112.07391/) (2021)
- [To Bias or Not to Bias: Detecting bias in News with bias-detector](https://mlsanity.com/paper/2505.13010/) (2025)
- [Sentence-level Media Bias Analysis with Event Relation Graph](https://mlsanity.com/paper/2404.01722/) (2024)
- [BEADs: Bias Evaluation Across Domains](https://mlsanity.com/paper/2406.04220/) (2024)
- [Experiments in News Bias Detection with Pre-Trained Neural Transformers](https://mlsanity.com/paper/2406.09938/) (2024)
- [Improved Models for Media Bias Detection and Subcategorization](https://mlsanity.com/paper/2412.11835/) (2024)
- [Bias Mitigation Agent: Optimizing Source Selection for Fair and Balanced Knowledge Retrieval](https://mlsanity.com/paper/2508.18724/) (2025)
- [An Approach to Ensure Fairness in News Articles](https://mlsanity.com/paper/2207.03938/) (2022)
- [Sparse Probability of Agreement](https://mlsanity.com/paper/2208.06161/) (2022)
- [Designing Closed-Loop Models for Task Allocation](https://mlsanity.com/paper/2305.19864/) (2023)
- [Responsible AI in NLP: GUS-Net Span-Level Bias Detection Dataset and Benchmark for Generalizations, Unfairness, and Stereotypes](https://mlsanity.com/paper/2410.08388/) (2024)
- [Tracing Partisan Bias to Its Emotional Fingerprints: A Computational Approach to Mitigation](https://mlsanity.com/paper/2501.01284/) (2025)
- [Definitional Sensitivity in Media Bias Detection: A Multi-Definition Dataset and Benchmark](https://mlsanity.com/paper/2608.23095/) (2026)
- [Recognizing Is Not Reversing: A Controlled Inversion Test of Fact-Preserving News Framing](https://mlsanity.com/paper/2609.11769/) (2026)

## Abstract (arXiv)

Many people consider news articles to be a reliable source of information on current events. However, due to the range of factors influencing news agencies, such coverage may not always be impartial. Media bias, or slanted news coverage, can have a substantial impact on public perception of events, and, accordingly, can potentially alter the beliefs and views of the public. The main data gap in current research on media bias detection is a robust, representative, and diverse dataset containing annotations of biased words and sentences. In particular, existing datasets do not control for the individual background of annotators, which may affect their assessment and, thus, represents critical information for contextualizing their annotations. In this poster, we present a matrix-based methodology to crowdsource such data using a self-developed annotation platform. We also present MBIC (Media Bias Including Characteristics) - the first sample of 1,700 statements representing various media bias instances. The statements were reviewed by ten annotators each and contain labels for media bias identification both on the word and sentence level. MBIC is the first available dataset about media bias reporting detailed information on annotator characteristics and their individual background. The current dataset already significantly extends existing data in this domain providing unique and more reliable insights into the perception of bias. In future, we will further extend it both with respect to the number of articles and annotators per article.