Understanding archetypes of fake news via fine grained classification
Fake news, doubtful statements and other unreliable content not only differ with regard to the level of misinformation but also with respect to the underlying intents. Prior work on algorithmic truth assessment has mostly pursued binary classifiers factual versus fake and disregarded these finer shades of untruth. In manual analyses of questionable content, in contrast, more fine grained distinctions have been proposed, such as distinguishing between hoaxes, irony and propaganda or the six way truthfulness ratings by the PolitiFact community. In this paper, we present a principled automated approach to distinguish these different cases while assessing and classifying news articles and claims. Our method is based on a hierarchy of five different kinds of fakeness and systematically explores a variety of signals from social media, capturing both the content and language of posts and the sharing and dissemination among users. The paper provides experimental results on the performance of our fine grained classifier and a detailed analysis of the underlying features.
Social Network Analysis and Mining