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.
To date, there are three important lines of research into the automated classification of genuine and fake news items. First, on a conceptual level, a distinction has been made between 'three types of fake news' @cite_11 : serious fabrications (i.e. news items about false and non-existing events or information such as celebrity gossip), hoaxes (i.e. providing false information via, for example, social media with the intention to be picked up by traditional news websites) and satire (i.e. humorous news items that mimic genuine news but contain irony and absurdity). Here, we focus on the first category, serious fabrication, in the two domains of general news (in six different categories), as well as on celebrity gossip.
@cite_11
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A fake news detection system aims to assist users in detecting and filtering out varieties of potentially deceptive news. The prediction of the chances that a particular news item is intentionally deceptive is based on the analysis of previously seen truthful and deceptive news. A scarcity of deceptive news, available as corpora for predictive modeling, is a major stumbling block in this field of natural language processing (NLP) and deception detection. This paper discusses three types of fake news, each in contrast to genuine serious reporting, and weighs their pros and cons as a corpus for text analytics and predictive modeling. Filtering, vetting, and verifying online information continues to be essential in library and information science (LIS), as the lines between traditional news and online information are blurring.