Amutha, R (2024) Detection on early dynamic rumor influence and propagation using biogeography‑based optimization with deep learning approaches. Detection on early dynamic rumor influence and propagation using biogeography‑based optimization with deep learning approaches, 83. pp. 82089-82106.

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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 remem
bering the earlier state with n consideration to the next context information. Also, a major
ity 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 exam
ining multiple Deep Learning approaches, with a focus on forward and backward direc
tion 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 recommenda
tions 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.

Item Type: Article
Uncontrolled Keywords: Convolutional Neural Network · Dynamic Rumor Influence · Propagation Detection Model · Bidirectional Long Short-Term Memory and biogeography-based optimization
Divisions: PSG College of Arts and Science > Department of Computer Science
Depositing User: Dr. B Sivakumar
Date Deposited: 07 Nov 2025 09:25
Last Modified: 07 Nov 2025 09:27
URI: https://ir.psgcas.ac.in/id/eprint/2516

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