Simultaneous Fake News and Topic Classification via Auxiliary Task Learning

Tsun Hin Cheung, Kin Man Lam

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Using social media, in particular, reading news articles, has become a necessary daily activity and an important way of spreading information. Classification of topics of new articles can provide up-to-date information about the current state of politics and society. However, this convenient way of sharing information can lead to the growth of falsification. Therefore, distinguishing between real and fake news, as well as fake-news classification, have become essential and indispensable. In this paper, we propose a new and up-to-date dataset for both fake-news classification and topic classification. To the best of our knowledge, we are the first to construct a dataset with both fake-news and topic labels, and employ multitask learning for learning these two tasks simultaneously. We have collected 21K online news articles published from January 2013 to March 2020. We propose an auxiliary-task long shortterm memory (AT-LSTM) neural network for text classification via multi-task learning. We evaluate and compare our proposed model to five baseline methods, via both single-task and multitask learning, on this new benchmark dataset. Experimental results show that our proposed AT-LSTM model outperforms the single-task learning methods and the hard parametersharing multi-task learning methods. The dataset and codes will be released in the future.

Original languageEnglish
Title of host publication2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages376-380
Number of pages5
ISBN (Electronic)9789881476883
Publication statusPublished - 7 Dec 2020
Event2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Virtual, Auckland, New Zealand
Duration: 7 Dec 202010 Dec 2020

Publication series

Name2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings

Conference

Conference2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020
Country/TerritoryNew Zealand
CityVirtual, Auckland
Period7/12/2010/12/20

Keywords

  • fake-news classification
  • multi-task learning
  • topic classification
  • web data mining

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Signal Processing
  • Decision Sciences (miscellaneous)
  • Instrumentation

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