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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

Identification of Spam on Social Media by Semi-supervised Learning Approach

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-66044-3_10,
        author={Sudana Shashi Kiran and Sure Uday Kiran and Elluru Amrutha and E. Yasaswi and Annam Kumar Sai and Kusam Sravya and S. R. Reeja},
        title={Identification of Spam on Social Media by Semi-supervised Learning Approach},
        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={Semi-Supervised Learning Social Media Ham And Spam Messages},
        doi={10.1007/978-3-031-66044-3_10}
    }
    
  • Sudana Shashi Kiran
    Sure Uday Kiran
    Elluru Amrutha
    E. Yasaswi
    Annam Kumar Sai
    Kusam Sravya
    S. R. Reeja
    Year: 2024
    Identification of Spam on Social Media by Semi-supervised Learning Approach
    PERSOM
    Springer
    DOI: 10.1007/978-3-031-66044-3_10
Sudana Shashi Kiran1, Sure Uday Kiran1, Elluru Amrutha1, E. Yasaswi1, Annam Kumar Sai1, Kusam Sravya1, S. R. Reeja1,*
  • 1: School of Computer Science and Engineering
*Contact email: reeja.sr@vitap.ac.in

Abstract

This study examines how machine learning methods can be used to identify Twitter spammers. Due to spammers’ increased use of social media platforms, it is crucial to combat their fraudulent operations. This study uses extensive Twitter data, such as user profiles, tweet content, and network connections, to create algorithms that accurately differentiate between genuine users and spammers. By successfully identifying and filtering out spammers utilizing a range of machine learning algorithms, deep learning techniques, feature extraction methodologies, and even categorizing tweets based on their emotional tone, the ultimate goal is to improve platform trust and user experience. In order to efficiently identify spammers and fake users, the article provides a solution that makes use of a unique machine learning algorithm that meticulously analyses user personal information and account history. Because of its popularity, Twitter has drawn spammers and bogus users, which has caused confusion. The study seeks to address “Twitter Spam Drift” by applying a Semi-Supervised Learning Approach (SSLA), which adjusts to changes in spam behavior and has demonstrated promising results on English datasets. To identify spam behaviors on Twitter, the project uses deep-learning classifier algorithms like LSTM, and GRU. Here, as can be seen, we use various methods to achieve accuracy ranging from 87% to 97%.

Keywords
Semi-Supervised Learning Social Media Ham And Spam Messages
Published
2024-08-13
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-66044-3_10
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