Abstract
The increased propagation of fake news is the significant concern in the digital era. Identification of fake news from social media platforms is critical to strengthen public trust and ensure social stability. This research presents an effective and accurate framework for identifying fake news that combines different steps of natural language processing (NLP) technique along with a neural network architecture. A novel semantic veracity enhancement (SVE) classifier is designed and implemented in this work for detecting fake news. The proposed approach leverages the effectiveness of sentiment analysis for identifying misleading or deceptive content and its subsequent implications on the sentiment and behaviour of social media users. A BERT model is used in this research for analysing the sentiments and classifying the texts from the social media platform. By examining the sentiments, the SVE classifier differentiates between real news and fabricated content. To achieve this, three different datasets comprising both actual content and fabricated (tweaked) tweets are employed for training the SVE classifier. The potentiality of the SVE classifier is evaluated and compared with different optimization techniques. The outcome of the experimental analysis shows that the proposed approach exhibits an excellent performance in terms of classifying misinformation from the original information with an outstanding accuracy of 99% compared to other state of art methods.
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MRS was involved in conceptualization, methodology, software, data curation, validation, writing—original draft, and writing—review and editing. NK was involved in visualization, investigation, supervision, validation, formal analysis, project administration, and resources.
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Sethurajan, M.R., Natarajan, K. Performance analysis of semantic veracity enhance (SVE) classifier for fake news detection and demystifying the online user behaviour in social media using sentiment analysis. Soc. Netw. Anal. Min. 14, 36 (2024). https://doi.org/10.1007/s13278-024-01199-9
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DOI: https://doi.org/10.1007/s13278-024-01199-9