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Detecting tag spams for social bookmarking Websites using a text mining approach

Author

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  • Hsin-Chang Yang

    (Department of Information Management, National University of Kaohsiung, Kaohsiung, Taiwan)

  • Chung-Hong Lee

    (Department of Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan)

Abstract

Social bookmarking Websites are popular nowadays for they provide platforms that are easy and clear to browse and organize Web pages. Users can add tags on Web pages to allow easy comprehension and retrieval of Web pages. However, tag spams could also be added to promote the opportunity of being referenced of a Web page, which is troublesome to users for accessing uninterested Web pages. In this work, we proposed a scheme to automatically detect such tag spams using a proposed text mining approach based on self-organizing map (SOM) model. We used SOM to find the associations among Web pages as well as tags. Such associations were then used to discover the relationships between Web pages and tags. Tag spams can then be detected according to such relationships. Experiments were conducted on a set of Web pages collected from a social bookmarking site and obtained promising result.

Suggested Citation

  • Hsin-Chang Yang & Chung-Hong Lee, 2014. "Detecting tag spams for social bookmarking Websites using a text mining approach," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 387-406.
    • Handle: RePEc:wsi:ijitdm:v:13:y:2014:i:02:n:s0219622014500473
    DOI: 10.1142/S0219622014500473
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    Cited by:

    1. Bahareh Rahmati & Mohammad Karim Sohrabi, 2019. "A Systematic Survey on High Utility Itemset Mining," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(04), pages 1113-1185, July.
    2. Luisa Bosetti, 2015. "Engaging stakeholders through Facebook. The case of Global Compact LEAD participants," Proceedings of Business and Management Conferences 3005158, International Institute of Social and Economic Sciences.

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