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

  • Research Article-Computer Engineering and Computer Science
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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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Notes

  1. http://www.twittermonitor.net/.

  2. https://www.cs.waikato.ac.nz/ml/weka/downloading.html.

  3. https://figshare.com/articles/PHEME_dataset_of_rumo-urs_and_nonrumours/4010619.

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Authors and Affiliations

  1. Computer Science Department, College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

    Abdullah Alsaeedi

  2. Information System Department, College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

    Mohammed Al-Sarem

  3. Information System Department, Saba’a Region Universitry, Mareeb, Yemen

    Mohammed Al-Sarem

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  1. Abdullah Alsaeedi
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  2. Mohammed Al-Sarem
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Correspondence to Abdullah Alsaeedi.

Appendix

Appendix

Table 26 Summary of the results attained after conducting the preliminary experiments using the PHEME dataset with all events
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Table 27 Summary of the results attained after conducting the final experiments using the PHEME dataset with all events with final setting (pooling = GlobalMaxPooling, activation method = tanh, optimizer= Adagrad, no-dropout, embedding dimension = 400, window size = 5, filter size = 100, number of units in dense layer = 100)
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Table 28 Summary of the results obtained using the proposed CNN based on the Ferguson event in the PHEME dataset
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Table 29 Summary of the results obtained using the proposed CNN based on the Sydney Siege event in the PHEME dataset
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Table 30 Summary of the results obtained using the proposed CNN based on the Charlie Hebdo event
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Table 31 Summary of the results obtained using the proposed CNN based on the Germanwings Crash event
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Table 32 Summary of the results obtained using the proposed CNN based on the Ottawa Shooting event
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Alsaeedi, A., Al-Sarem, M. Detecting Rumors on Social Media Based on a CNN Deep Learning Technique. Arab J Sci Eng 45, 10813–10844 (2020). https://doi.org/10.1007/s13369-020-04839-2

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