Synthesis of Online Criminal User Behaviours Disseminating Bengali Fake News Using Sentiment Analysis

Title

Synthesis of Online Criminal User Behaviours Disseminating Bengali Fake News Using Sentiment Analysis

Creator

Sethurajan M.R.; Natarajan K.

Description

Even though research on artificial intelligence (AI) is still in its early phases, the field is growing in popularity. We created a hybrid machine learning model to better understand the pattern of results connected to illegal user behaviour. Then, after identifying the components of illegal user activity, we created a theory for forecasting criminal user behaviour that explains the patterns and results. Our study focuses on offenders spreading misleading information online and makes use of a Bengali dataset. Sentiment analysis is a modern technology that can help us understand how individuals feel in different scenarios during their everyday lives. To comprehend the pattern behind this agenda, machine learning and deep learning techniques will be applied throughout the categorization process. To determine the possible attitudes driving criminal conduct that spreads misleading information, sentiment levels on social media may be monitored or studied. This study examines the use of several artificial intelligence approaches to assess sentiment in social media data in order to identify criminal user activity occurring throughout the world. The hybrid model CNN with Adam optimizer exhibits higher precision levels while doing sentiment analysis. In addition to identifying solutions to the issues that people currently face in the modern world, we also propose a new categorization system for illicit user activity. In our analysis of the research's shortcomings, we make recommendations for a broader research agenda on illicit user conduct and how one can forecast the criminal user behaviour on psychological aspects. Our model was thus able to draw 87.33% accuracy in determining criminal behaviour patterns. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Source

Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 377-393.

Date

2024-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Artificial intelligence; Deep learning; Fake news; Machine learning; Sentiment analysis

Coverage

Sethurajan M.R., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bengaluru, 560074, India; Natarajan K., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bengaluru, 560074, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-981970974-8

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-97-0975-5_35

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197226736&doi=10.1007%2f978-981-97-0975-5_35&partnerID=40&md5=b4ec8181e852a62caa34dc60ec76bec0