Abstract
The far and wide distribution of innumerable rumors and fake news have been a serious threat to the truthfulness of microblogs. The earlier works have frequently aimed at remembering the earlier state with n consideration to the next context information. Also, a majority of the works before have made use of conventional feature representation approaches preceding a classifier. In this research, we evaluate the rumor detection problem by examining multiple Deep Learning approaches, with a focus on forward and backward direction analysis. The proposed technique incorporates Optimal Bidirectional Long Short-Term Memory and Convolutional Neural Network in order to correctly classify tweets as rumor or non-rumor. Then the Biogeography-based optimization (BBO) provides recommendations for fine-tuning the Bi-LSTM-CNN model's hyperparameters. According to the results of the experiments, the suggested technique is more precise than conventional methods, with an accuracy of 86.12%. The statistical analysis further demonstrates that the suggested model is much more successful than the appropriate alternatives.
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Amutha, R. Detection on early dynamic rumor influence and propagation using biogeography-based optimization with deep learning approaches. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18168-1
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DOI: https://doi.org/10.1007/s11042-024-18168-1