A deep neural networks-based fusion model for COVID-19 rumor detection from online social media
Data Technologies and Applications
ISSN: 2514-9288
Article publication date: 22 April 2022
Issue publication date: 9 December 2022
Abstract
Purpose
The COVID-19 has become a global pandemic, which has caused large number of deaths and huge economic losses. These losses are not only caused by the virus but also by the related rumors. Nowadays, online social media are quite popular, where billions of people express their opinions and propagate information. Rumors about COVID-19 posted on online social media usually spread rapidly; it is hard to analyze and detect rumors only by artificial processing. The purpose of this paper is to propose a novel model called the Topic-Comment-based Rumor Detection model (TopCom) to detect rumors as soon as possible.
Design/methodology/approach
The authors conducted COVID-19 rumor detection from Sina Weibo, one of the most widely used Chinese online social media. The authors constructed a dataset about COVID-19 from January 1 to June 30, 2020 with a web crawler, including both rumor and non-rumors. The rumor detection task is regarded as a binary classification problem. The proposed TopCom model exploits the topical memory networks to fuse latent topic information with original microblogs, which solves the sparsity problems brought by short-text microblogs. In addition, TopCom fuses comments with corresponding microblogs to further improve the performance.
Findings
Experimental results on a publicly available dataset and the proposed COVID dataset have shown superiority and efficiency compared with baselines. The authors further randomly selected microblogs posted from July 1–31, 2020 for the case study, which also shows the effectiveness and application prospects for detecting rumors about COVID-19 automatically.
Originality/value
The originality of TopCom lies in the fusion of latent topic information of original microblogs and corresponding comments with DNNs-based models for the COVID-19 rumor detection task, whose value is to help detect rumors automatically in a short time.
Keywords
Acknowledgements
Funding: This research was funded by the National Key Research and Development Program of China [No. 2020YFA0908300], the National Natural Science Foundation of China [Grant No. 62002137, 61876072], the Fundamental Research Funds for the Central Universities [No. JUSRP12021], and the State Key Lab. for Novel Software Technology, Nanjing University, P.R. China [No. KFKT2020B02].
Citation
Lu, H.-y., Yang, J., Fang, W., Song, X. and Wang, C. (2022), "A deep neural networks-based fusion model for COVID-19 rumor detection from online social media", Data Technologies and Applications, Vol. 56 No. 5, pp. 806-824. https://doi.org/10.1108/DTA-06-2021-0160
Publisher
:Emerald Publishing Limited
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