DocNet: Semantic Structure in Inductive Bias Detection Models
<|reference_start|>DocNet: Semantic Structure in Inductive Bias Detection Models: News will have biases so long as people have opinions. However, as social media becomes the primary entry point for news and partisan gaps increase, it is increasingly important for informed citizens to be able to identify bias. People will be able to take action to avoid polarizing echo chambers if they know how the news they are consuming is biased. 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 and data are made available at https://github.com/nlpresearchanon.<|reference_end|>
@article{zhu2024docnet:,
    title={DocNet: Semantic Structure in Inductive Bias Detection Models},
    author={Jessica Zhu, Iain Cruickshank, Michel Cukier},
    journal={arXiv preprint arXiv:2406.10965},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10965},
    primaryClass={cs.CL cs.SI}
}