Abstract
Automatic satire detection is a subtle text classification task, for machines and at times, even for humans. In this paper we argue that satire detection should be approached using common-sense inferences, rather than traditional text classification methods. We present a highly structured latent variable model capturing the required inferences. The model abstracts over the specific entities appearing in the articles, grouping them into generalized categories, thus allowing the model to adapt to previously unseen situations.
Highlights
Satire is a writing technique for passing criticism using humor, irony or exaggeration
We look into the satire detection task (Burfoot and Baldwin, 2009), predicting if a given news article is real or satirical, and suggest that this prediction task should be defined over common-sense inferences, rather than looking at it as a lexical text classification task (Pang and Lee, 2008; Burfoot and Baldwin, 2009), which bases the decision on word-level features
Since our goal is to identify satirical articles, given significantly more real articles, we report the Fmeasure of the positive class
Summary
Satire is a writing technique for passing criticism using humor, irony or exaggeration. It is often used in contemporary politics to ridicule individual politicians, political parties or society as a whole. Satirical writing often builds on real facts and expectations, pushed to absurdity to express humorous insights about the situation. The difference between real and satirical articles can be subtle and often confusing to readers. With the recent rise of social media outlets, satirical articles have become increasingly popular and have famously fooled several leading news agencies. With the recent rise of social media outlets, satirical articles have become increasingly popular and have famously fooled several leading news agencies1
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