Ensembles of Recurrent Networks for Classifying the Relationship of Fake News Titles

Su, T., Macdonald, C. and Ounis, I. (2019) Ensembles of Recurrent Networks for Classifying the Relationship of Fake News Titles. In: 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'19), Paris, France, 21-25 Jul 2019, pp. 893-896. ISBN 9781450361729 (doi: 10.1145/3331184.3331305)

Abstract

Nowadays, everyone can create and publish news and information anonymously online. However, the credibility of such news and information are not guaranteed. To differentiate fake news from genuine news, one can compare a recent news with earlier posted ones. Identified suspicious news can be debunked to stop the fake news from spreading further. In this paper, we investigate the advantages of recurrent neural networks-based language representations (e.g., BERT, BiLSTM) in order to build ensemble classifiers that can accurately predict if one news title is related to, and, additionally disagrees with an earlier news title. Our experiments, on a dataset of 321k news titles created for the WSDM 2019 challenge, show that the BERT-based models significantly outperform BiLSTM, which in-turn significantly outperforms a simpler embedding-based representation. Furthermore, even the state-of-the-art BERT approach can be enhanced when combined with a simple BM25 feature.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Su, Ting and Macdonald, Professor Craig and Ounis, Professor Iadh
• Authors: Su, T., Macdonald, C., and Ounis, I.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9781450361729
• Copyright Holders: Copyright © 2019 The Authors
• First Published: First published in Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'19): 893-896
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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