NewsUnfold: Creating a News-Reading Application That Indicates Linguistic Media Bias and Collects Feedback
<|reference_start|>NewsUnfold: Creating a News-Reading Application That Indicates Linguistic Media Bias and Collects Feedback: Media bias is a multifaceted problem, leading to one-sided views and impacting decision-making. A way to address digital media bias is to detect and indicate it automatically through machine-learning methods. However, such detection is limited due to the difficulty of obtaining reliable training data. Human-in-the-loop-based feedback mechanisms have proven an effective way to facilitate the data-gathering process. Therefore, we introduce and test feedback mechanisms for the media bias domain, which we then implement on NewsUnfold, a news-reading web application to collect reader feedback on machine-generated bias highlights within online news articles. Our approach augments dataset quality by significantly increasing inter-annotator agreement by 26.31% and improving classifier performance by 2.49%. As the first human-in-the-loop application for media bias, the feedback mechanism shows that a user-centric approach to media bias data collection can return reliable data while being scalable and evaluated as easy to use. NewsUnfold demonstrates that feedback mechanisms are a promising strategy to reduce data collection expenses and continuously update datasets to changes in context.<|reference_end|>
@article{hinterreiter2024newsunfold:,
    title={NewsUnfold: Creating a News-Reading Application That Indicates
  Linguistic Media Bias and Collects Feedback},
    author={Smi Hinterreiter, Martin Wessel, Fabian Schliski, Isao Echizen, Marc
  Erich Latoschik, Timo Spinde},
    journal={arXiv preprint arXiv:2407.17045},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.17045},
    primaryClass={cs.HC}
}