Abstract: The blurry line between nefarious fake news and protected-speech satire has been a notorious struggle for social media platforms. Further to the efforts of reducing exposure to misinformation on social media, purveyors of fake news have begun to masquerade as satire sites to avoid being demoted. In this work, we address the challenge of automatically classifying fake news versus satire. Previous work have studied whether fake news and satire can be distinguished based on language differences. Contrary to fake news, satire stories are usually humorous and carry some political or social message. We hypothesize that these nuances could be identified using semantic and linguistic cues. Consequently, we train a machine learning method using semantic representation, with a state-of-the-art contextual language model, and with linguistic features based on textual coherence metrics. Empirical evaluation attests to the merits of our approach compared to the language-based baseline and sheds light on the nuances between fake news and satire. As avenues for future work, we consider studying additional linguistic features related to the humor aspect, and enriching the data with current news events, to help identify a political or social message.

Introduction: The efforts by social media platforms to reduce the exposure of users to misinformation have resulted, on several occasions, in flagging legitimate satire stories. To avoid penalizing publishers of satire, which is a protected form of speech, the platforms have begun to add more nuance to their flagging systems. Facebook, for instance, added an option to mark content items as “Satire”, if “the content is posted by a page or domain that is a known satire publication, or a reasonable person would understand the content to be irony or humor with a social message” BIBREF0. This notion of humor and social message is also echoed in the definition of satire by Oxford dictionary as “the use of humour, irony, exaggeration, or ridicule to expose and criticize people's stupidity or vices, particularly in the context of contemporary politics and other topical issues”. The distinction between fake news and satire carries implications with regard to the exposure of content on social media platforms. While fake news stories are algorithmically suppressed in the news feed, the satire label does not decrease the reach of such posts. This also has an effect on the experience of users and publishers. For users, incorrectly classifying satire as fake news may deprive them from desirable entertainment content, while identifying a fake news story as legitimate satire may expose them to misinformation. For publishers, the distribution of a story has an impact on their ability to monetize content. Moreover, in response to these efforts to demote misinformation, fake news purveyors have begun to masquerade as legitimate satire sites, for instance, carrying small badges at the footer of each page denoting the content as satire BIBREF1. The disclaimers are usually small such that the stories are still being spread as though they were real news BIBREF2. This gives rise to the challenge of classifying fake news versus satire based on the content of a story. While previous work BIBREF1 have shown that satire and fake news can be distinguished with a word-based classification approach, our work is focused on the semantic and linguistic properties of the content. Inspired by the distinctive aspects of satire with regard to humor and social message, our hypothesis is that using semantic and linguistic cues can help to capture these nuances. Our main research questions are therefore, RQ1) are there semantic and linguistic differences between fake news and satire stories that can help to tell them apart?; and RQ2) can these semantic and linguistic differences contribute to the understanding of nuances between fake news and satire beyond differences in the language being used? The rest of paper is organized as follows: in section SECREF2, we briefly review studies on fake news and satire articles which are the most relevant to our work. In section SECREF3, we present the methods we use to investigate semantic and linguistic differences between fake and satire articles. Next, we evaluate these methods and share insights on nuances between fake news and satire in section SECREF4. Finally, we conclude the paper in section SECREF5 and outline next steps and future work.

Question: What empirical evaluation was used?
Answer: Empirical  evaluation  has done using 10 fold cross-validation considering semantic representation with BERT and  measuring   differences between fake news and satire  using coherence metric.