Access the full text.
Sign up today, get DeepDyve free for 14 days.
References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.
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.
International Journal of Enterprise Network Management – Inderscience Publishers
Published: Jan 1, 2022
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.