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In this manuscript, a deep learning-based Generative Adversarial Networks (GAN) classifier optimized with the Water Strider Optimization Algorithm (WSOA) is proposed to solve the issue of fake tweets in Twitter. Initially, input Twitter data is taken from the "Twitter API" dataset. Then, pre-processing is done in the Twitter dataset. After that, the pre-processed tweet features are selected based on the Bag of Words model, mutual information, and the Chi-square method. Then the selected features are given into the Generative Adversarial Networks classifier for detecting fake tweets. Here, the Generative Adversarial Networks classifier is used to fake tweets detection. But it does not expose any optimization strategies adopted to determine the optimum parameters to confirm the accurate categorization of fake tweets. Therefore, Water Strider Optimization Algorithm is applied to optimize the parameter in Generative Adversarial Networks classifier (FT-GAN-WSOAC). The proposed method is activated in MATLAB and the performance is analyzed with performance metrics. The experimental results show that the proposed FT-GAN-WSOA classifier provides higher accuracy of 25.14%, 25.21%, 20.44%, and 34.38% than the existing methods, such as fake tweet detection with content-based features using a custom rule-based algorithm (FT-CBF-CRBA) method, fake tweet detection with long/short-term memory using a random forest algorithm (FT-LSTM-RFA) method, fake tweet detection with zero-shot learning using bidirectional encoder representations from transformers (FT-ZSL-BERT) method, and fake tweet detection with a hybrid convolution neural network and recurrent neural network with long/short-term memory (FT-Hyb CNN-RNN-LSTM) method, respectively. [ABSTRACT FROM AUTHOR]