We’re excited to announce that our team, in person of Prof. Dr. Bela Gipp, University of Göttingen, and Prof. Dr. Michael Granitzer, University of Passau, has been awarded a prestigious research grant of close to €700,000 by the German Research Foundation (DFG) to develop BARI—the Bias Analysis Research Infrastructure. The project brings together researchers from…
		We reviewed, summarized, and organized the AI and other computational methods from 2019 to 2022 for detecting media bias into six categories. 7 Min Read  ||  View Original Paper || Our Github Main Takeaways: We developed six categories to systematically organize computational methods for detecting various subtypes of media bias, providing a comprehensive framework for evaluating…
		The Media Bias Taxonomy aims to clarify the various subtypes of media bias and standardize the interdisciplinary terminology used to describe them. 8 min read  ||  View Original Paper || Our Github  Main Takeaways: We introduce the Media Bias Taxonomy—an interactive framework designed to explore different media bias types. Media bias is present not only…
		We developed a questionnaire to assess readers' perceptions of media bias  through research, reduction, and validation. 10 min read  ||  View Original Paper || Media Bias Questionnaire Main Takeaways: We developed a 48-question questionnaire to measure how readers perceive media bias, which can be used in media studies and annotation processes. We semantically grouped the questions…
		BABE—the dataset of Bias Annotations By Experts—is one of the largest media bias datasets with gold-standard expert annotations at the word and sentence levels. 13 min read  ||  View Original Paper || Our Github Main Takeaways: We release BABE, one of the largest media bias datasets fully annotated by trained media bias experts, providing word-…
		