2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 14 , Issue 2 , PP: 146-158, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Nadezda Pospelova 1 * , Aiziryak Tarasova 2 , Natalya Subbotina 3 , Natalya Koroleva 4 , Nilufar Raimova 5 , E. Laxmi Lydia 6
Doi: https://doi.org/10.54216/FPA.140212
Deceptive content recognition in social media employing artificial intelligence (AI) includes the use of sophisticated techniques and machine learning (ML) methods to recognize deceptive or wrong data shared on numerous platforms. AI methods analyse textual as well as multimedia content, investigative patterns, linguistic cues, and contextual info to flag latent cases of deception. As a result of the use of natural language processing (NLP) and computer vision (CV), these systems identify subtle nuances, misrepresentation strategies, and anomalies in user-generated content. This active technique permits social media platforms, organizations, and consumers to recognize and diminish the spread of deceptive content, donates to a more reliable online atmosphere, and aids in fighting tasks modelled by misinformation and false news. This study offers a novel sine cosine algorithm with deep learning-based deceptive content detection on social media (SCADL-DCDSM) technique. The SCADL-DCDSM technique incorporates the ensemble learning process with a hyperparameter tuning strategy for classifying the sentiments. Primarily, the SCADL-DCDSM technique pre-processes the input data to change the input data into a valuable format. Moreover, the SCADL-DCDSM algorithm follows the BERT model for the word embedding process. Moreover, the SCADL-DCDSM technique involves an ensemble of three models for sentiment classification such as long short-term memory (LSTM), extreme learning machine (ELM), and attention-based recurrent neural network (ARNN). Finally, SCA can be executed for better hyperparameter choice of the DL models. The SCADL-DCDSM system integrates the explainable artificial intelligence (XAI) system LIME has been employed for a comprehensive explainability and understanding of the black-box process, enhancing correct deceptive content recognition. The simulation result analysis of the SCADL-DCDSM algorithm has been examined on a benchmark database. The simulation outcome illustrated that the SCADL-DCDSM methodology achieves optimum solution than other approaches in terms of different measures.
Social Media , Word Embedding , Explainable Artificial Intelligence , BERT , Natural Language Processing
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