@inproceedings{Hinterreiter2025NewsUnfold,

title = {NewsUnfold: Creating a News-Reading Application That Indicates Linguistic Media Bias and Collects Feedback},

author = {Smi Hinterreiter and Martin Wessel and Fabian Schliski and Isao Echizen and Marc Erich Latoschik and Timo Spinde},

url = {https://media-bias-research.org/wp-content/uploads/2024/07/Preprint_ICWSM_25_NewsUnfold.pdf},

year  = {2025},

date = {2025-06-01},

urldate = {2025-06-01},

booktitle = {Proceedings of the International AAAI Conference on Web and Social Media (ICWSM'25)},

volume = {19},

publisher = {AAAI},

address = {Copenhagen, Denmark},

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},

keywords = {crowdsourcing, HITL, linguistic bias, media bias, news bias},

pubstate = {forthcoming},

tppubtype = {inproceedings}

}