The rise of digital media enhances information accessibility but also introduces challenges related to the quality and impartiality of news reporting, particularly regarding biases that influence public perception during key global events. In response, this study introduces LLNewsBias, a dataset designed to detect and analyze political bias in multilingual news headlines, covering four major events from 2019 to 2022—Brexit, COVID-19, the 2020 U.S. election, and the Ukraine-Russia war. With over 350,000 headlines in 17 languages, annotated with bias labels, this dataset is compiled using Media Bias/Fact Check and Event Registry. Our contributions include a structured framework for data collection and organization, enabling event-wise and year-wise analysis while supporting lifelong learning. We also highlight potential use cases that demonstrate the dataset’s utility in advancing bias prediction models, multilingual adaptation, and model robustness. Additionally, we discuss the dataset’s limitations, addressing po- tential biases, sample size constraints, and contextual factors. This work provides a valuable resource for improving bias detec- tion in dynamic, multilingual news environments, contributing to the development of more accurate and adaptable models in natural language processing and media studies.     «

The rise of digital media enhances information accessibility but also introduces challenges related to the quality and impartiality of news reporting, particularly regarding biases that influence public perception during key global events. In response, this study introduces LLNewsBias, a dataset designed to detect and analyze political bias in multilingual news headlines, covering four major events from 2019 to 2022—Brexit, COVID-19, the 2020 U.S. election, and the Ukraine-Russia war. Wit...     »