RDNN: Rumor Detection Neural Network for Veracity Analysis in Social Media Text

• SuthanthiraDevi P,
• Karthika S,

Vol. 16, No. 12, December 31, 2022
10.3837/tiis.2022.12.005, Download Paper (Free):

• Attention Mechanism
• Bidirectional Long Short Term Memory (Bi-LSTM)
• Convolution Neural Network (CNN)
• Deep Learning
• Natural Language Processing

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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