DocNet: Semantic Structure in Inductive Bias Detection Models

• URL: http://arxiv.org/abs/2406.10965v2
• Date: Sun, 17 Nov 2024 17:30:24 GMT
• Title: DocNet: Semantic Structure in Inductive Bias Detection Models
• Authors: Jessica Zhu, Iain Cruickshank, Michel Cukier,
• Abstract summary: In this paper, we explore an often overlooked aspect of bias detection in documents: the semantic structure of news articles. We present DocNet, a novel, inductive, and low-resource document embedding and bias detection model. We also demonstrate that the semantic structure of news articles from opposing partisan sides, as represented in document-level graph embeddings, have significant similarities.
• Score: 0.4779196219827508
• License:
• Abstract: News will have biases so long as people have opinions. It is increasingly important for informed citizens to be able to identify bias as social media becomes the primary entry point for news and partisan differences increase. If people know the biases of the news they are consuming, 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 documents: the semantic structure of news articles. We present DocNet, a novel, inductive, and low-resource document embedding and bias detection model that outperforms large language models. We also demonstrate that the semantic structure of news articles from opposing partisan sides, as represented in document-level graph embeddings, have significant similarities. These results can be used to advance bias detection in low-resource environments. Our code, data, and the corresponding datasheet are made available at: https://anonymous.4open.science/r/DocNet/.

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