Incendiary News Detection

In this work we introduce the problem of incendiary news detection. We compare and contrast this problem with the problem of hate speech detection in social media. Most of the social media posts that are classified as hate speech contain straightforward slurs, insults, swearing, etc. In contrast to social media posts, incendiary news articles often do not contain any straightforward slurs and insults but, nevertheless, incite hate. To detect such news articles, we leverage are source where activists attempt to combat hate on-line by manually tagging the news articles inciting hate. We collect non-incendiary news by retrieving news articles from the websites of the news agencies which are recognized world-wide as serious media that are highly unlikely to contain foul language (BBC, CNN). We run a classification experiment using several classification approaches. We demonstrate that our system differentiates between incendiary and non-incendiary news with 97.0% accuracy. We ensure the validity of our approach by using two different non-incendiary news corpora.