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Pervasive Knowledge and Collective Intelligence on Web and Social Media. Second EAI International Conference, PerSOM 2023, Hyderabad, India, November 24–25, 2023, Proceedings

Research Article

Behavioural Analysis in Web Pattern Mining of Social Media Networks Using Deep DenseNet Classification

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-66044-3_15,
        author={Biju Balakrishnan and B. Shanthini and R. Amudha},
        title={Behavioural Analysis in Web Pattern Mining of Social Media Networks Using Deep DenseNet Classification},
        proceedings={Pervasive Knowledge and Collective Intelligence on Web and Social Media. Second EAI International Conference, PerSOM 2023, Hyderabad, India, November 24--25, 2023, Proceedings},
        proceedings_a={PERSOM},
        year={2024},
        month={8},
        keywords={Behavioral Analysis Web Pattern Mining Deep Learning Social Media Networks User Behavior DenseNet Classification},
        doi={10.1007/978-3-031-66044-3_15}
    }
    
  • Biju Balakrishnan
    B. Shanthini
    R. Amudha
    Year: 2024
    Behavioural Analysis in Web Pattern Mining of Social Media Networks Using Deep DenseNet Classification
    PERSOM
    Springer
    DOI: 10.1007/978-3-031-66044-3_15
Biju Balakrishnan1,*, B. Shanthini1, R. Amudha2
  • 1: St. Peter’s Institute of Higher Education and Research, Avadi
  • 2: Department of Information Technology, Hindusthan College of Engineering and Technology
*Contact email: bijujctcse123@gmail.com

Abstract

Web pattern mining in social media networks has gained significant attention due to the abundance of user-generated content. Understanding user behaviors and preferences within these networks is crucial for personalized recommendations, targeted marketing, and content optimization. In this study, we propose a novel approach for behavioral analysis in web pattern mining of social media networks using deep DenseNet classification. We formulate the task as a multi-class classification problem, where each class corresponds to a specific user behavior pattern. Our proposed approach leverages the expressive power of DenseNet, a deep neural network architecture, to automatically learn intricate features from raw web data, capturing both local and global patterns. We present a comprehensive experimental evaluation on a real-world social media dataset, demonstrating the effectiveness of our approach in accurately classifying diverse user behaviors. The results highlight the superiority of deep DenseNet classification over traditional methods, showcasing its potential for enhancing behavioral analysis in the context of web pattern mining.

Keywords
Behavioral Analysis Web Pattern Mining Deep Learning Social Media Networks User Behavior DenseNet Classification
Published
2024-08-13
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-66044-3_15
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