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Deep Level Analysis of Legitimacy in Bengali News Sentences

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Published:29 November 2021Publication History
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Abstract

The tremendous increase in the growth of misinformation in news articles has the potential threat for the adverse effects on society. Hence, the detection of misinformation in news data has become an appealing research area. The task of annotating and detecting distorted news article sentences is the immediate need in this research direction. Therefore, an attempt has been made to formulate the legitimacy annotation guideline followed by annotation and detection of the legitimacy in Bengali e-papers. The sentence-level manual annotation of Bengali news has been carried out in two levels, namely “Level-1 Shallow Level Classification” and “Level-2 Deep Level Classification” based on semantic properties of Bengali sentences. The tagging of 1,300 anonymous Bengali e-paper sentences has been done using the formulated guideline-based tags for both levels. The validation of the annotation guideline has been done by applying benchmark supervised machine learning algorithms using the lexical feature, syntactic feature, domain-specific feature, and Level-2 specific feature in both levels. Performance evaluation of these classifiers is done in terms of Accuracy, Precision, Recall, and F-Measure. In both levels, Support Vector Machine outperforms other benchmark classifiers with an accuracy of 72% and 65% in Level-1 and Level-2, respectively.

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            cover image ACM Transactions on Asian and Low-Resource Language Information Processing
            ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 21, Issue 1
            January 2022
            442 pages
            ISSN:2375-4699
            EISSN:2375-4702
            DOI:10.1145/3494068
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            Publication History

            • Published: 29 November 2021
            • Accepted: 1 April 2021
            • Revised: 1 March 2021
            • Received: 1 September 2020
            Published in tallip Volume 21, Issue 1

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