Attending Sentences to detect Satirical Fake News

Proceedings of the 27th International Conference on Computational Linguistics, pages 3371–3380 Santa Fe, New Mexico, USA, August 20-26, 2018. 3371 Attending Sentences to detect Satirical Fake News Sohan De Sarkar Dept. of Computer Science Indian Institute of Technology Kharagpur, West Bengal, India sohandesarkar@gmail.com Fan Yang Dept. of Computer Science University of Houston 3551 Cullen Blvd., Houston fyang11@uh.edu Arjun Mukherjee Dept. of Computer Science University of Houston 3551 Cullen Blvd., Houston arjun4787@gmail.com Abstract Satirical news detection is important in order to prevent the spread of misinformation over the Internet. Existing approaches to capture news satire use machine learning models such as SVM and hierarchical neural networks along with hand-engineered features, but do not explore sen- tence and document difference. This paper proposes a robust, hierarchical deep neural network approach for satire detection, which is capable of capturing satire both at the sentence level and at the document level. The architecture incorporates pluggable generic neural networks like CNN, GRU, and LSTM. Experimental results on real world news satire dataset show substantial per- formance gains demonstrating the effectiveness of our proposed approach. An inspection of the learned models reveals the existence of key sentences that control the presence of satire in news. 1 Introduction In the era of the Internet, online journalism is now a common practice. Online news articles have a major contribution in keeping people informed about what is happening in the world. The usage of Internet to spread news comes with the disadvantage of deception. The presence of deceptive and misleading news articles has been around for a while. Although some news articles often have a disclaimer about it being fake, many other don’t and thus readers could be led to believe them to be true. This leads to spread of misinformation, which may also start off a rumour. The importance of the detection of deceptive news is increasing rapidly, as more and more people start relying on online news as their major source of news. News satire is a genre of deceptive news that is found on the web, with the intent of dispensing satire in the form of legitimate news articles. These articles differ from “fake” news, in the sense that fake news intend to mislead people by providing untrue facts, while satirical news intends to ridicule and criticize something by providing satirical comments or through fictionalized stories. Satire is the intention of the author to be discovered as “fake”, unlike fake news, in which the intention is to make make the readers believe in the news as true. Detection of news satire is thus important to control the spread of false stories. We propose a hierarchical deep neural network model for satirical news detection, that is able to capture satire both at the sentence level and at the document level. The architecture is very extensible and caters to a variety of plug-and-play neural network models such as CNN, LSTM and GRU. This pipelined architecture allows for optimal learning of parameters required to capture satire. We show that …

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