Predicting Factuality of Reporting and Bias of News Media Sources
원문 초록
We present a study on predicting the factuality of reporting and bias of news media. While previous work has focused on studying the veracity of claims or documents, here we are interested in characterizing entire news media. These are under-studied but arguably important research problems, both in their own right and as a prior for fact-checking systems. We experiment with a large list of news websites and with a rich set of features derived from (i) a sample of articles from the target news medium, (ii) its Wikipedia page, (iii) its Twitter account, (iv) the structure of its URL, and (v) information about the Web traffic it attracts. The experimental results show sizable performance gains over the baselines, and confirm the importance of each feature type.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
From Generation to Detection: A Multimodal Multi-Task Dataset for Benchmarking Health Misinformation
Communication Styles and Reader Preferences of LLM and Human Experts in Explaining Health Information
Computational Fact-Checking of Online Discourse: Scoring scientific accuracy in climate change related news articles
Can LLMs Automate Fact-Checking Article Writing?
Fuzzy Deep Hybrid Network for Fake News Detection
AI Wizards at CheckThat! 2025: Enhancing Transformer-Based Embeddings with Sentiment for Subjectivity Detection in News Articles
토론
이 논문과 연결된 포럼 글이 아직 없습니다.