About the Dataset Media bias is a multifaceted problem, leading to one-sided views and impacting decision-making. A way to address bias in news articles is to automatically detect and indicate it through machine-learning methods. However, such detection is limited due to the difficulty of obtaining reliable training data. To facilitate the data-gathering process, we introduce NewsUnravel, a news-reading web application leveraging an initially tested feedback mechanism to collect reader feedback on machine-generated bias highlights within 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, NewsUnravel shows that a user-centric approach to media bias data collection can return reliable data while being scalable and evaluated as easy to use. NewsUnravel demonstrates that feedback mechanisms are a promising strategy to reduce data collection expenses, fluidly adapt to changes in language, and enhance evaluators' diversity. Description of the data files This repository contains the datasets for the anonymous NewsUnravel submission. The tables contain following data: NUDAdataset.csv: the NUDA dataset with 310 new sentences with bias labels Statistics.png: contains all Umami statistics for NewsUnravel's usage data Feedback.csv: holds the participantID of a single feedback with the sentence ID (contentId), the bias rating, and provided reasons Content.csv: holds the participant ID of a rating with the sentence ID (contentId) of a rated sentences and the bias rating, and reason, if given Article.csv: holds the article ID, title, source, article meta data, article topic, and bias amount in % Participant.csv: holds the participant IDs and data processing consent