Identifying Rumor Sources in Social Networks Using Hashing Vectorizer for Text Representation

Authors

  • N. Gopika MCA Student, Department of Computer Application, KMM Institute of Post Graduate Studies, Ramireddipalle, Tirupati (D.t), Andhra Pradesh, India Author
  • Dr. K. Venkataramana Professor, Department of Computer Application, KMM Institute of Post Graduate Studies, Ramireddipalle, Tirupati (D.t), Andhra Pradesh, India Author

Keywords:

Rumor detection, Rumor source identification, Hashing Vectorizer, Gaussian Naive Bayes, Social networks, Flask

Abstract

In today's digitally connected world, the rapid dissemination of rumors and misinformation across social networks presents significant challenges to public trust and information integrity. This study introduces a novel approach for Rumor Source Identification (RSI) using text analytics and network analysis techniques. We employ the Hashing Vectorizer for efficient and scalable text representation, enabling us to process large volumes of social media data. For classification, we utilize the Gaussian Naive Bayes algorithm to determine whether a message constitutes a rumor or not. Our methodology focuses on tracking the diffusion patterns of information and identifying the original sources responsible for initiating rumor cascades. The frontend of our system is developed using HTML, CSS, and JavaScript, while the backend is implemented with Flask, enabling a user-friendly and interactive platform for visualization and analysis. This research contributes to the development of effective tools for early rumor detection and source tracing, ultimately aiming to mitigate the spread of misinformation and enhance the reliability of online information.

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References

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Published

22-05-2025

Issue

Vol. 11 No. 3 (2025): May-June

Section

Research Articles

License

Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology

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This work is licensed under a Creative Commons Attribution 4.0 International License.