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Arabian Journal for Science and Engineering
Erschienen in: Arabian Journal for Science and Engineering 12/2020

29.08.2020 | Research Article-Computer Engineering and Computer Science

Detecting Rumors on Social Media Based on a CNN Deep Learning Technique

verfasst von: Abdullah Alsaeedi, Mohammed Al-Sarem

Erschienen in: Arabian Journal for Science and Engineering | Ausgabe 12/2020

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Abstract

Currently, it is easy to create content and share it via social media platforms such as Twitter, Facebook, and Sina Weibo. However, some problems can occur when the shared content includes untrustworthy or misleading information. Thus, researchers from different domains have tried to investigate the impact of rumors on the global community. Several machine learning approaches have been used to detect rumors at their early stage. However, the achieved accuracies demonstrate that the existing state-of-the-art rumor detection approaches still require improvement. In this paper, we propose a deep learning model based on a conventional neural network (CNN) to detect rumors spreading on Twitter. Several experiments were conducted to find the best hyperparameter settings to improve the model’s performance. We compared our results with other relevant rumor detection approaches that used the same publicly available benchmark dataset to demonstrate our model’s performance regarding the accuracy, precision, recall and f-measure. The results show that our CNN model outperforms all the existing approaches and achieves the best balance of recall and precision.
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Metadaten
Titel
Detecting Rumors on Social Media Based on a CNN Deep Learning Technique
verfasst von
Abdullah Alsaeedi
Mohammed Al-Sarem
Publikationsdatum
29.08.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
Arabian Journal for Science and Engineering / Ausgabe 12/2020
Print ISSN: 2193-567X
Elektronische ISSN: 2191-4281
DOI
https://doi.org/10.1007/s13369-020-04839-2

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