Computational Assessment of Hyperpartisanship in News Titles

arXiv:2301.06270v2 Announce Type: replace
Abstract: We first adopt a human-guided machine learning framework to develop a new dataset for hyperpartisan news title detection with 2,200 manually labeled and 1.8 million machine-labeled titles that were posted from 2014 to the present by nine representative media organizations across three media bias groups - Left, Central, and Right in an active learning manner. A fine-tuned transformer-based language model achieves an overall accuracy of 0.84 and an F1 score of 0.78 on an external …

abstract arxiv assessment bias computational cs.cl dataset detection framework human machine machine learning media organizations type