Generative Adversarial Networks Classifier Optimized with Water Strider Algorithm for Fake Tweets Detection

Abstract

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.

KEYWORDS:

• Bag of words
• Chi-square method
• Fake tweets
• Generative adversarial networks classifier
• Micro-blogging platform
• Mutual information
• Twitter
• Twitter API
• Water strider optimization algorithm

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

V. Muthulakshmi

V Muthulakshmi is currently working as a professor in Department of Information Technology, St Joseph’s College of Engineering, Anna University, Semmencherry, Chennai, Tamil Nadu, India. She possesses 26 years of experience in teaching and research. She received her PhD degree from Anna University, Chennai, India. She holds a Bachelor of Engineering (BE) in electronics and telecommunication engineering, and a master’s degree in computer science and engineering. Her interests include artificial intelligence, machine learning, and cloud computing. She has published several papers in national and international refereed journals and conferences. She is a member of various professional organizations such as Computer Society of India (CSI), Indian Society for Technical Education (ISTE).

Francis H. Shajin

Francis H Shajin graduated from Anna University, India. He has over 10 years of experience in research and development field. He has published over 35 papers in international journals. His current research interests include very-large-scale integration, soft computing, image processing, machine learning and networking. Email: [email protected]

J. Dhiviya Rose

J Dhiviya Rose received BTech degree in CSE from St Xavier’s Catholic College of Engineering, Nagercoil which was then affiliated to Manonmanium Sundaranar University, Tirunelveli (Tamil Nadu, India) in 2003 and MTech degree in CSE from Karunya University, Coimbatore (Tamil Nadu, India) in 2010. Currently, she is assistant professor (Sr Gr) at Cybernetics Cluster, School of Computer Science, Energy Acres, Bidholi, Dehradun, Uttarakhand, India and pursuing PhD. Her research interests are in networking, multi-agent systems, artificial intelligence, and web technology. Email: [email protected]

P. Rajesh

P Rajesh graduated from Anna University, India. He has over 10 years of experience in research and development field. He has published more than 35 papers in international journals. His current research interests include artificial intelligence, power system, smart grid technologies and soft computing. Email: [email protected]