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Continual Learning for Fake News Detection from Social Media

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Artificial Neural Networks and Machine Learning – ICANN 2021 (ICANN 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12892))

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Abstract

The prevalence of fake news over social media has a profound impact on justice, public trust and society as a whole. Although significant effort has been applied to mitigate its negative impact, our study shows that existing fake news detection algorithms may perform poorly on new data. In other words, the performance of a model trained on one dataset degrades on another and potentially vastly different dataset. Considering that in practice a deployed fake news detection system is likely to observe unseen data, it is crucial to solve this problem without re-training the model on the entire data from scratch, which would become prohibitively expensive as the data volumes grow. An intuitive solution is to further train the model on the new dataset, but our results show that this direct incremental training approach does not work, as the model only performs well on the latest dataset it is trained on, which is similar to the problem of catastrophic forgetting in the field of continual learning. Instead, in this work, (1) we first demonstrate that with only minor computational overhead, balanced performance can be restored on both existing and new datasets, by utilising Gradient Episodic Memory (GEM) and Elastic Weight Consolidation (EWC)—two techniques from continual learning. (2) We improve the algorithm of GEM so that the drop in model performance on the previous task can be further minimised. Specifically, we investigate different techniques to optimise the sampling process for GEM, as an improvement over random selection as originally designed. (3) We conduct extensive experiments on two datasets with thousands of labelled news items to verify our results.

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Notes

  1. 1.

    Here we use the definition in [38]: fake news is intentionally and verifiably false news published by a news outlet.

  2. 2.

    Node \(i\) is published before node \(j\), and the information goes from user \(i\) to user \(j\).

  3. 3.

    We have also tested \(K=200, 500, 1000, \infty \) (not clipped). Those results are omitted due to space limits (the results are better under those settings).

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Authors and Affiliations

  1. School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia

    Yi Han, Shanika Karunasekera & Christopher Leckie

Authors
  1. Yi Han
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  2. Shanika Karunasekera
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  3. Christopher Leckie
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Correspondence to Yi Han .

Editor information

Editors and Affiliations

  1. Comenius University in Bratislava, Bratislava, Slovakia

    Igor Farkaš

  2. iMotions A/S, Copenhagen, Denmark

    Paolo Masulli

  3. University of Tübingen, Tübingen, Baden-Württemberg, Germany

    Sebastian Otte

  4. Universität Hamburg, Hamburg, Germany

    Stefan Wermter

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Han, Y., Karunasekera, S., Leckie, C. (2021). Continual Learning for Fake News Detection from Social Media. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12892. Springer, Cham. https://doi.org/10.1007/978-3-030-86340-1_30

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  • DOI: https://doi.org/10.1007/978-3-030-86340-1_30

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-86340-1

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