 Bangladeshi False message detection using the Tree and characterized by procrastination classifiersRasidul Haque (), Md. Shafiul Alam Chowdhury (), Md. Farukuzzaman Khan (), Mohammed Sowket Ali (), Md. Amanat Ullah () and Md. Abdul Mannan () Edelweiss Applied Science and Technology, 2024, vol. 8, issue 6, 6132-6146 Abstract: False message is much moreexotericdue to the rapid use of social media. The ability to detect False message is recognized among the riskiest types of manipulation, as it is formed with the malicious purpose of misleading consumers. Many researchers have already suggested several False message detection systems based on social context and diffusion. In this paper, we have presented a system that can expose False message related to the subject of Bangladeshi digital message content. Moreover, machine learning classifiers named Extra tree and Procrastination have been selected. For feature extractions, we have used Term-Frequency-Inverse Document Frequency (TF-IDF) and countvectorizer simultaneously, and countvectorizer separately. After doing two types of experiments, the Extra tree classifier gives the best result for the first experiment and the Procrastination classifier gives the average result for the first experiment,where both classifiers give the average result for the second experiment. Keywords: GNB; LR; MLP; RF; SVM; TF-IDF matrix; VEC. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ajp:edwast:v:8:y:2024:i:6:p:6132-6146:id:3346 Access Statistics for this article More articles in Edelweiss Applied Science and Technology from Learning Gate |
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