# Modeling Multi-level Context for Informational Bias Detection by Contrastive Learning and Sentential Graph Network

arXiv: https://arxiv.org/abs/2201.10376 · HTML: https://mlsanity.com/paper/2201.10376/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 25 Jan 2022.

## Builds on (most cited first, 11 of 11)

- [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)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [We Can Detect Your Bias: Predicting the Political Ideology of News Articles](https://mlsanity.com/paper/2010.05338/) (2020)
- [Predicting Factuality of Reporting and Bias of News Media Sources](https://mlsanity.com/paper/1810.01765/) (2018)
- [In Plain Sight: Media Bias Through the Lens of Factual Reporting](https://mlsanity.com/paper/1909.02670/) (2019)
- [Pretrained Language Models for Sequential Sentence Classification](https://mlsanity.com/paper/1909.04054/) (2019)
- [Multi-view Models for Political Ideology Detection of News Articles](https://mlsanity.com/paper/1809.03485/) (2018)
- [SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compression](https://mlsanity.com/paper/2007.08954/) (2020)
- [Detecting Media Bias in News Articles using Gaussian Bias Distributions](https://mlsanity.com/paper/2010.10649/) (2020)
- [Context in Informational Bias Detection](https://mlsanity.com/paper/2012.02015/) (2020)

## Built on by (most cited first, 5 of 5)

- [The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias](https://mlsanity.com/paper/2312.16148/) (2023)
- [Connecting the Dots in News Analysis: Bridging the Cross-Disciplinary Disparities in Media Bias and Framing](https://mlsanity.com/paper/2309.08069/) (2023)
- [Developing a Natural Language Understanding Model to Characterize Cable News Bias](https://mlsanity.com/paper/2310.09166/) (2023)
- [A Commonsense-Infused Language-Agnostic Learning Framework for Enhancing Prediction of Political Polarity in Multilingual News Headlines](https://mlsanity.com/paper/2212.00298/) (2022)
- [Target-Aware Contextual Political Bias Detection in News](https://mlsanity.com/paper/2310.01138/) (2023)

## Abstract (arXiv)

Informational bias is widely present in news articles. It refers to providing one-sided, selective or suggestive information of specific aspects of certain entity to guide a specific interpretation, thereby biasing the reader's opinion. Sentence-level informational bias detection is a very challenging task in a way that such bias can only be revealed together with the context, examples include collecting information from various sources or analyzing the entire article in combination with the background. In this paper, we integrate three levels of context to detect the sentence-level informational bias in English news articles: adjacent sentences, whole article, and articles from other news outlets describing the same event. Our model, MultiCTX (Multi-level ConTeXt), uses contrastive learning and sentence graphs together with Graph Attention Network (GAT) to encode these three degrees of context at different stages by tactically composing contrastive triplets and constructing sentence graphs within events. Our experiments proved that contrastive learning together with sentence graphs effectively incorporates context in varying degrees and significantly outperforms the current SOTA model sentence-wise in informational bias detection.