# Sentence-level Media Bias Analysis with Event Relation Graph

arXiv: https://arxiv.org/abs/2404.01722 · HTML: https://mlsanity.com/paper/2404.01722/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 58 references that are in the map.
- First posted on arXiv 2 Apr 2024.

## Builds on (most cited first, 22 of 22)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [Event Extraction by Answering (Almost) Natural Questions](https://mlsanity.com/paper/2004.13625/) (2020)
- [We Can Detect Your Bias: Predicting the Political Ideology of News Articles](https://mlsanity.com/paper/2010.05338/) (2020)
- [A Multi-Axis Annotation Scheme for Event Temporal Relations](https://mlsanity.com/paper/1804.07828/) (2018)
- [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)
- [MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction](https://mlsanity.com/paper/2211.07342/) (2022)
- [Neural Media Bias Detection Using Distant Supervision With BABE -- Bias Annotations By Experts](https://mlsanity.com/paper/2209.14557/) (2022)
- [Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction](https://mlsanity.com/paper/1909.05360/) (2019)
- [POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection](https://mlsanity.com/paper/2205.00619/) (2022)
- [Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution](https://mlsanity.com/paper/1906.01753/) (2019)
- [MBIC -- A Media Bias Annotation Dataset Including Annotator Characteristics](https://mlsanity.com/paper/2105.11910/) (2021)
- [Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement](https://mlsanity.com/paper/2106.01654/) (2021)
- [Detecting Media Bias in News Articles using Gaussian Bias Distributions](https://mlsanity.com/paper/2010.10649/) (2020)
- [GraphPlan: Story Generation by Planning with Event Graph](https://mlsanity.com/paper/2102.02977/) (2021)
- [Context in Informational Bias Detection](https://mlsanity.com/paper/2012.02015/) (2020)
- [Identifying Conspiracy Theories News based on Event Relation Graph](https://mlsanity.com/paper/2310.18545/) (2023)
- [Predicting Sentence-Level Factuality of News and Bias of Media Outlets](https://mlsanity.com/paper/2301.11850/) (2023)
- [Discourse Structures Guided Fine-grained Propaganda Identification](https://mlsanity.com/paper/2310.18544/) (2023)

## Built on by (most cited first, 3 of 3)

- [The Promises and Pitfalls of LLM Annotations in Dataset Labeling: a Case Study on Media Bias Detection](https://mlsanity.com/paper/2411.11081/) (2024)
- [Multi-document Summarization through Multi-document Event Relation Graph Reasoning in LLMs: a case study in Framing Bias Mitigation](https://mlsanity.com/paper/2506.12978/) (2025)
- [A Multi-View Media Profiling Suite: Resources, Evaluation, and Analysis](https://mlsanity.com/paper/2605.01336/) (2026)

## Abstract (arXiv)

Media outlets are becoming more partisan and polarized nowadays. In this paper, we identify media bias at the sentence level, and pinpoint bias sentences that intend to sway readers' opinions. As bias sentences are often expressed in a neutral and factual way, considering broader context outside a sentence can help reveal the bias. In particular, we observe that events in a bias sentence need to be understood in associations with other events in the document. Therefore, we propose to construct an event relation graph to explicitly reason about event-event relations for sentence-level bias identification. The designed event relation graph consists of events as nodes and four common types of event relations: coreference, temporal, causal, and subevent relations. Then, we incorporate event relation graph for bias sentences identification in two steps: an event-aware language model is built to inject the events and event relations knowledge into the basic language model via soft labels; further, a relation-aware graph attention network is designed to update sentence embedding with events and event relations information based on hard labels. Experiments on two benchmark datasets demonstrate that our approach with the aid of event relation graph improves both precision and recall of bias sentence identification.