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

Hsin-Chang Yang () and Chung-Hong Lee
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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

International Journal of Information Technology & Decision Making (IJITDM), 2014, vol. 13, issue 02, 387-406

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.

Keywords: Social spam detection; text mining; self-organizing map; social bookmarking (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (2)

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DOI: 10.1142/S0219622014500473

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