Vol. 16, No. 12, December 31, 2022
10.3837/tiis.2022.12.005,
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Abstract
A widely used social networking service like Twitter has the ability to disseminate information to large groups of people even during a pandemic. At the same time, it is a convenient medium to share irrelevant and unverified information online and poses a potential threat to society. In this research, conventional machine learning algorithms are analyzed to classify the data as either non-rumor data or rumor data. Machine learning techniques have limited tuning capability and make decisions based on their learning. To tackle this problem the authors propose a deep learning-based Rumor Detection Neural Network model to predict the rumor tweet in real-world events. This model comprises three layers, AttCNN layer is used to extract local and position invariant features from the data, AttBi-LSTM layer to extract important semantic or contextual information and HPOOL to combine the down sampling patches of the input feature maps from the average and maximum pooling layers. A dataset from Kaggle and ground dataset #gaja are used to train the proposed Rumor Detection Neural Network to determine the veracity of the rumor. The experimental results of the RDNN Classifier demonstrate an accuracy of 93.24% and 95.41% in identifying rumor tweets in real-time events.
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Cite this article
[IEEE Style]
S. P and K. S, "RDNN: Rumor Detection Neural Network for Veracity Analysis in Social Media Text," KSII Transactions on Internet and Information Systems, vol. 16, no. 12, pp. 3868-3888, 2022. DOI: 10.3837/tiis.2022.12.005.
[ACM Style]
SuthanthiraDevi P and Karthika S. 2022. RDNN: Rumor Detection Neural Network for Veracity Analysis in Social Media Text. KSII Transactions on Internet and Information Systems, 16, 12, (2022), 3868-3888. DOI: 10.3837/tiis.2022.12.005.
[BibTeX Style]
@article{tiis:38210, title="RDNN: Rumor Detection Neural Network for Veracity Analysis in Social Media Text", author="SuthanthiraDevi P and Karthika S and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2022.12.005}, volume={16}, number={12}, year="2022", month={December}, pages={3868-3888}}