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arXiv · 2407.17045

NewsUnfold: Creating a News-Reading Application That Indicates Linguistic Media Bias and Collects Feedback

Smi Hinterreiter·Martin Wessel·Fabian Schliski·Isao Echizen·Marc Erich Latoschik·Timo Spinde
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Abstract

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.

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Smi Hinterreiter, Martin Wessel, Fabian Schliski, Isao Echizen, Marc Erich Latoschik, Timo Spinde. 2024-07-29. NewsUnfold: Creating a News-Reading Application That Indicates Linguistic Media Bias and Collects Feedback. https://arxiv.org/abs/2407.17045

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