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Profile matching of online users across multiple social networks: a text mining approach

Profile matching of online users across multiple social networks: a text mining approach Profile matching of a person using various online social networks is a non-trivial task. Major challenges in developing a reliable and scalable matching scheme include the non-availability of the required information or having contradictory information for the same user across these networks. In this study, we propose a method that utilises the contents generated by or shared with users across their online social networks. With the help of text mining techniques, we extract the high frequency words and common high frequency words in the user's posts/tweets (content attributes). Based on experiments with real datasets, this method provides 72.5% accuracy in identity matching amongst user's profiles. Given the data, we develop classification models, and we achieved accuracy and F1 score of 72.5% and 67.0%, respectively. This study will be helpful to enhance the accuracy of the identity resolution frameworks. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Enterprise Network Management Inderscience Publishers

Profile matching of online users across multiple social networks: a text mining approach

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1748-1252
eISSN
1748-1260
DOI
10.1504/ijenm.2022.122402
Publisher site
See Article on Publisher Site

Abstract

Profile matching of a person using various online social networks is a non-trivial task. Major challenges in developing a reliable and scalable matching scheme include the non-availability of the required information or having contradictory information for the same user across these networks. In this study, we propose a method that utilises the contents generated by or shared with users across their online social networks. With the help of text mining techniques, we extract the high frequency words and common high frequency words in the user's posts/tweets (content attributes). Based on experiments with real datasets, this method provides 72.5% accuracy in identity matching amongst user's profiles. Given the data, we develop classification models, and we achieved accuracy and F1 score of 72.5% and 67.0%, respectively. This study will be helpful to enhance the accuracy of the identity resolution frameworks.

Journal

International Journal of Enterprise Network ManagementInderscience Publishers

Published: Jan 1, 2022

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