Automatic Detection of Fake News
This work addresses the challenge of identifying trustworthy news sources in online media, which is crucial for combating misinformation, but it is incremental as it builds on existing detection methods with new datasets.
The paper tackled the problem of automatically detecting fake news by introducing two novel datasets covering seven domains and conducting learning experiments to build accurate detectors, achieving results that provide comparative analyses between automatic and manual identification.
The proliferation of misleading information in everyday access media outlets such as social media feeds, news blogs, and online newspapers have made it challenging to identify trustworthy news sources, thus increasing the need for computational tools able to provide insights into the reliability of online content. In this paper, we focus on the automatic identification of fake content in online news. Our contribution is twofold. First, we introduce two novel datasets for the task of fake news detection, covering seven different news domains. We describe the collection, annotation, and validation process in detail and present several exploratory analysis on the identification of linguistic differences in fake and legitimate news content. Second, we conduct a set of learning experiments to build accurate fake news detectors. In addition, we provide comparative analyses of the automatic and manual identification of fake news.