mlsanityData to 14 Oct 2026

DocNet: Semantic Structure in Inductive Bias Detection Models

arXiv 2406.10965 ↗First posted 16 Jun 2024cs.CL cs.SIcited by 0

Not on the map. Why?

Abstract (arXiv)

News will be biased so long as people have opinions. As social media becomes the primary entry point for news and partisan differences increase, it is increasingly important for informed citizens to be able to recognize bias. If people are aware of the biases of the news they consume, they will be able to take action to avoid polarizing echo chambers. In this paper, we explore an often overlooked aspect of bias detection in media: the semantic structure of news articles. We present DocNet, a novel, inductive, and low-resource document embedding and political bias detection model. We also demonstrate that the semantic structure of news articles from opposing political sides, as represented in document-level graph embeddings, have significant similarities. DocNet bypasses the need for pre-trained language models, reducing resource dependency while achieving comparable performance. It can be used to advance political bias detection in low-resource environments. Our code and data are made available at: https://anonymous.4open.science/r/DocNet/

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 27 of its 81 references that are in the map. First posted on arXiv 16 Jun 2024.

Builds on

27 of 81 references.

References · 27

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2018Datasheets for Datasetscited by 1,386
  3. 2016Variational Graph Auto-Encoderscited by 1,382
  4. 2014Distributed Representations of Sentences and Documentscited by 678
  5. 2018Graph Convolutional Networks for Text Classificationcited by 345
  6. 2019In Plain Sight: Media Bias Through the Lens of Factual Reportingcited by 68
  7. 2022Neural Media Bias Detection Using Distant Supervision With BABE -- Bias Annotations By Expertscited by 56
  8. 2022POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detectioncited by 51
  9. 2020What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Contextcited by 42
  10. 2020Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Mediacited by 28
  11. 2022Dbias: Detecting biases and ensuring Fairness in news articlescited by 28
  12. 2019Message Passing Attention Networks for Document Understandingcited by 23
15 more
  1. 2020Detecting Media Bias in News Articles using Gaussian Bias Distributionscited by 19
  2. 2023Introducing MBIB -- the first Media Bias Identification Benchmark Task and Dataset Collectioncited by 18
  3. 2022A Domain-adaptive Pre-training Approach for Language Bias Detection in Newscited by 16
  4. 2021Sparse Structure Learning via Graph Neural Networks for Inductive Document Classificationcited by 15
  5. 2022InducT-GCN: Inductive Graph Convolutional Networks for Text Classificationcited by 15
  6. 2023Graph Neural Networks for Text Classification: A Surveycited by 15
  7. 2024IndiVec: An Exploration of Leveraging Large Language Models for Media Bias Detection with Fine-Grained Bias Indicatorscited by 15
  8. 2024Realistic Evaluation of Toxicity in Large Language Modelscited by 15
  9. 2022Exploiting Transformer-based Multitask Learning for the Detection of Media Bias in News Articlescited by 11
  10. 2023It's All in the Embedding! Fake News Detection Using Document Embeddingscited by 7
  11. 2023Predicting Sentence-Level Factuality of News and Bias of Media Outletscited by 6
  12. 2023Connecting the Dots in News Analysis: Bridging the Cross-Disciplinary Disparities in Media Bias and Framingcited by 6
  13. 2024Detection and Discovery of Misinformation Sources using Attributed Webgraphscited by 4
  14. 2023Analysis of Media Writing Style Bias through Text-Embedding Networkscited by 1
  15. 2023Crossing the Aisle: Unveiling Partisan and Counter-Partisan Events in News Reportingcited by 1

Built on by

Not cited yet.