UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 10 Issue 4
April-2023
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2304B25


Registration ID:
514034

Page Number

l147-l153

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Title

Identifying Potentially Radical Content through Semantic Analysis and Machine Learning for Social Media Applications

Abstract

With the emergence of social media as a common platform for communication among different people and communities at large, the chances for malicious usage of social media platforms for malicious activities has also increased manifold. One such malicious activity is spreading radical content over social media platforms due to the ease of sharing among several individuals and groups. The challenging aspect though for social media agencies or security agencies is the screening of humongous amounts of data to detect radical content. With no clear boundary to demarcate radical and non-radical content, the classification problem becomes challenging as the data size increases.[1] The proposed work presents an artificial intelligence based technique for detection of radical content. The proposed approach uses the concept of dictionary learning to train a Bayesian Regularized artificial neural network. The performance evaluation parameters are the number of iterations, absolute time and the accuracy. It has been shown that while the proposed system attains a classification accuracy of 97% compared to 89% of previous work.

Key Words

Identifying Potentially Radical Content through Semantic Analysis and Machine Learning for Social Media Applications

Cite This Article

"Identifying Potentially Radical Content through Semantic Analysis and Machine Learning for Social Media Applications", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 4, page no.l147-l153, April-2023, Available :http://www.jetir.org/papers/JETIR2304B25.pdf

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2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Identifying Potentially Radical Content through Semantic Analysis and Machine Learning for Social Media Applications", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 4, page no. ppl147-l153, April-2023, Available at : http://www.jetir.org/papers/JETIR2304B25.pdf

Publication Details

Published Paper ID: JETIR2304B25
Registration ID: 514034
Published In: Volume 10 | Issue 4 | Year April-2023
DOI (Digital Object Identifier):
Page No: l147-l153
Country: -, -, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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