An Improved Hippopotamus Optimization Algorithm with Trend-Aware Mechanism for Fake Information Detection.
4 affiliations
- 1School of Finance, Jilin University of Finance and Economics, Changchun 130117, China.
- 2Jilin Provincial Key Laboratory of Fintech, Changchun 130117, China.
- 3School of Health Management, Mudanjiang Medical University, Mudanjiang 157011, China.
- 4Institute of National Development and Security Studies, Jilin University, Changchun 130012, China.
Abstract
Fake information detection remains challenging because deceptive texts often contain complex semantic structures, while time-consuming hyperparameter tuning limits model performance. To address these issues, this study proposes IHOA-CNN-BiLSTM. This hybrid detection framework integrates the Improved Hippopotamus Optimization Algorithm (IHOA) with an optimized parallel dual-channel deep learning architecture to enhance detection accuracy. Firstly, the Logistic-Tent hybrid chaotic mapping replaces random population initialization, improving coverage uniformity and ergodicity in the search space. Secondly, the trend-aware mechanism (TAM) captures population evolution trends by maintaining historical search positions and a fitness matrix. An adaptive covariance matrix generates offset vectors that provide trend guidance during exploration, exploitation, and local optima escape stages, thereby balancing global and fine-grained local search. Thirdly, a CNN-BiLSTM parallel dual-channel feature-extraction network is designed, with key hyperparameters jointly optimized by IHOA to address fake information detection tasks. Experiments on the PolitiFact dataset show the proposed model achieves an accuracy of 90.13%, exceeding the optimal hybrid model by 2.36 percentage points. On the Weibo21 dataset, the accuracy and F1-score are 5.59 and 5.84 percentage points higher than the average values of the nine optimized CNN-BiLSTM baselines. Overall, this work presents a practical and efficient approach for detecting fake information.
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BibTeX
@article{dai2026improved,
title = {{An Improved Hippopotamus Optimization Algorithm with Trend-Aware Mechanism for Fake Information Detection.}},
author = {Xu Dai and Guoqiang Lu and Jiaxue Li},
journal = {Biomimetics (Basel, Switzerland)},
year = {2026},
month = oct,
doi = {10.3390/biomimetics11100697},
url = {https://europepmc.org/article/MED/42930249},
note = {CC BY},
}
RIS
TY - JOUR
TI - An Improved Hippopotamus Optimization Algorithm with Trend-Aware Mechanism for Fake Information Detection.
AU - Xu Dai
AU - Guoqiang Lu
AU - Jiaxue Li
PY - 2026
DA - 2026/10/01
JO - Biomimetics (Basel, Switzerland)
DO - 10.3390/biomimetics11100697
UR - https://europepmc.org/article/MED/42930249
AB - Fake information detection remains challenging because deceptive texts often contain complex semantic structures, while time-consuming hyperparameter tuning limits model performance. To address these issues, this study proposes IHOA-CNN-BiLSTM. This hybrid detection framework integrates the Improved Hippopotamus Optimization Algorithm (IHOA) with an optimized parallel dual-channel deep learning architecture to enhance detection accuracy. Firstly, the Logistic-Tent hybrid chaotic mapping replaces random population initialization, improving coverage uniformity and ergodicity in the search space. Secondly, the trend-aware mechanism (TAM) captures population evolution trends by maintaining historical search positions and a fitness matrix. An adaptive covariance matrix generates offset vectors that provide trend guidance during exploration, exploitation, and local optima escape stages, thereby balancing global and fine-grained local search. Thirdly, a CNN-BiLSTM parallel dual-channel feature-extraction network is designed, with key hyperparameters jointly optimized by IHOA to address fake information detection tasks. Experiments on the PolitiFact dataset show the proposed model achieves an accuracy of 90.13%, exceeding the optimal hybrid model by 2.36 percentage points. On the Weibo21 dataset, the accuracy and F1-score are 5.59 and 5.84 percentage points higher than the average values of the nine optimized CNN-BiLSTM baselines. Overall, this work presents a practical and efficient approach for detecting fake information.
SN - 2313-7673
ER -