Machine Learning Innovations for Effective Spam Comment Filtering in Social Networks

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Year : 2024 | Volume :02 | Issue : 02 | Page : –
By
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Sujata Patil,

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Pradudny Phase,

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Anand Walukar,

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Pushpak Pandey,

  1. Assistant Professor, Department of Electronics and Telecommunication engineering, Smt Kashibai Navale College of Engineering, SPPU, Pune, Maharashtra, India
  2. Student, Department of Electronics and Telecommunication engineering, Smt Kashibai Navale College of Engineering, SPPU, Pune, Maharashtra, India
  3. Student, Department of Electronics and Telecommunication engineering, Smt Kashibai Navale College of Engineering, SPPU, Pune, Maharashtra, India
  4. Assistant Professor, School of Technology Management & Engineering, SVKM’s NMIMS School of Technology and Science, Kharghar, Navi Mumbai, Maharashtra, India

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The increasing prevalence of social media platforms has revolutionized communication, fostering unparalleled levels of connectivity and data exchange. However, the widespread increase of spam comments presents a serious threat to the integrity of online discussions, potentially undermining the quality of interactions. To confront this issue, our proposed model utilizes machine learning techniques to bolster spam comment detection across various social media platforms. This endeavor involves a thorough investigation encompassing data aggregation and collection, feature engineering, model selection, and meticulous performance evaluation. The ultimate goal is to develop a resilient and precise system capable of effectively discerning genuine user comments from spam content. By leveraging advanced methodologies and algorithms using machine learning, our model aims to mitigate the disruptive impact of spam, preserving the authenticity of online conversations, and sustaining meaningful interactions. Through this comprehensive approach, we aspire to enhance user experience, foster trust in social media platforms, and contribute to the cultivation of a healthier digital environment by classifying real comments and spam comments. Our proposed system not only addresses the technical aspects of spam detection but also emphasizes the broader implications for user engagement and platform integrity as a strong system. By advancing spam detection capabilities with the help of advanced algorithms, we aim to protect the quality of social media interactions and ensure a more trustworthy and enjoyable online community. Ultimately, our work seeks to provide a scalable and effective solution that can be adapted to various social media environments, promoting a safer and more positive user experience.

Keywords: Machine learning, online conversation, real comments, spam comments, strong system

[This article belongs to International Journal of Algorithms Design and Analysis Review (ijadar)]

How to cite this article:
Sujata Patil, Pradudny Phase, Anand Walukar, Pushpak Pandey. Machine Learning Innovations for Effective Spam Comment Filtering in Social Networks. International Journal of Algorithms Design and Analysis Review. 2024; 02(02):-.
How to cite this URL:
Sujata Patil, Pradudny Phase, Anand Walukar, Pushpak Pandey. Machine Learning Innovations for Effective Spam Comment Filtering in Social Networks. International Journal of Algorithms Design and Analysis Review. 2024; 02(02):-. Available from: https://journals.stmjournals.com/ijadar/article=2024/view=0

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Regular Issue Subscription Review Article
Volume 02
Issue 02
Received 28/06/2024
Accepted 23/08/2024
Published 06/11/2024
Total Visits: 43

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