Abstract
Fake identity is a critical problem nowadays on social media. Fake news is rapidly spread by fake identities and bots that generate the trustworthiness issue on social media. Identifying profiles and accounts using soft computing algorithms is necessary to improve the trustworthiness of social media. The Recurrent Neural Network (RNN) categorizes each profile based on training and testing modules. This work focuses on classifying bots or human entries according to their extracted features using machine learning. Once the training phase is completed, features are extracted from the dataset based on the term frequency on which the classification technique is applied. The proposed work is very effective in detecting malicious accounts from an imbalanced dataset in social media. The system provides maximum accuracy for the classification of fake and real identities on the social media dataset. It achieves good accuracy with RNN long short-term memory (LSTM). The system improves the classification accuracy with the increase in the number of folds in cross-validation. In experiment analysis, we have done testing on real-time social media datasets; We achieve around 96% accuracy, 100% precision, 99% recall, and 96% F1 score on the real-time dataset.
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Chourasiya, K., Chaturvedi, K., Shrivastava, R. (2023). Optimization Accuracy on an Intelligent Approach to Detect Fake News on Twitter Using LSTM Neural Network. In: Bhattacharya, A., Dutta, S., Dutta, P., Piuri, V. (eds) Innovations in Data Analytics. ICIDA 2022. Advances in Intelligent Systems and Computing, vol 1442. Springer, Singapore. https://doi.org/10.1007/978-981-99-0550-8_8
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