An Improved Hippopotamus Optimization Algorithm with Trend-Aware Mechanism for Fake Information Detection

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.