Sentiment aware fake news detection on online social networks

AJAO, Seun, BHOWMIK, Deepayan and ZARGARI, Shahrzad (2019). Sentiment aware fake news detection on online social networks. 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

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Link to published version:: https://doi.org/10.1109/ICASSP.2019.8683170
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    Abstract

    Messages posted to online social networks (OSNs) causes a recent stir due to the intended spread of fake news or rumor. In this work, we aim to understand and analyse the characteristics of fake news especially in relation to sentiments, to determine the automatic detection of fake news and rumors. Based on empirical observation, we propose a hypothesis that there exists a relation between a fake message/rumour and the sentiment of the texts posted online. We verify our hypothesis by comparing with the state-of-the-art baseline text-only fake news detection methods that do not consider sentiments. We performed experiments on standard Twitter fake news dataset and show good improvements in detecting fake news/rumor.

    Item Type: Article
    Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Identification Number: https://doi.org/10.1109/ICASSP.2019.8683170
    Depositing User: Colin Knott
    Date Deposited: 12 Feb 2019 11:42
    Last Modified: 18 Mar 2021 00:22
    URI: http://shura.shu.ac.uk/id/eprint/24009

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