Zhang, Q;
Lipani, A;
Liang, S;
Yilmaz, E;
(2019)
Reply-Aided Detection of Misinformation via Bayesian Deep Learning.
In: Liu, Ling and White, Ryen, (eds.)
Proceedings of WWW '19 The World Wide Web Conference.
(pp. pp. 2333-2343).
ACM: New York, USA.
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Abstract
Social media platforms are a plethora of misinformation and its potential negative influence on the public is a growing concern. This concern has drawn the attention of the research community on developing mechanisms to detect misinformation. The task of misinformation detection consists of classifying whether a claim is True or False. Most research concentrates on developing machine learning models, such as neural networks, that outputs a single value in order to predict the veracity of a claim. One of the major problem faced by these models is the inability of representing the uncertainty of the prediction, which is due incomplete or finite available information about the claim being examined. We address this problem by proposing a Bayesian deep learning model. The Bayesian model outputs a distribution used to represent both the prediction and its uncertainty. In addition to the claim content, we also encode auxiliary information given by people’s replies to the claim. First, the model encodes a claim to be verified, and generate a prior belief distribution from which we sample a latent variable. Second, the model encodes all the people’s replies to the claim in a temporal order through a Long Short Term Memory network in order to summarize their content. This summary is then used to update the prior belief generating the posterior belief. Moreover, in order to train this model, we develop a Stochastic Gradient Variational Bayes algorithm to approximate the analytically intractable posterior distribution. Experiments conducted on two public datasets demonstrate that our model outperforms the state-of-the-art detection models.
| Type: | Proceedings paper |
|---|---|
| Title: | Reply-Aided Detection of Misinformation via Bayesian Deep Learning |
| Event: | WWW '19: World Wide Web Conference 2019, 13-17 May 2019, San Francisco, USA |
| ISBN-13: | 978-1-4503-6674-8 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3308558.3313718 |
| Publisher version: | https://doi.org/10.1145/3308558.3313718 |
| Language: | English |
| Additional information: | This paper is published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license (https://creativecommons.org/licenses/by/4.0/). |
| Keywords: | misinformation detection, bayesian analysis, deep learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10068786 |
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