Open Access

ARTICLE

A Machine Learning Approach to User Profiling for Data Annotation of Online Behavior

by Moona Kanwal^1,2,*, Najeed A. Khan¹, Aftab A. Khan³

1 Computer Science and Information Technology Department, NED University of Engineering and Technology, Karachi, Pakistan
2 Computer Engineering Department, Sir Syed University of Engineering and Technology, Karachi, Pakistan
3 College of Education, Health and Human Services, Longwood University, Farmville, VA, USA

* Corresponding Author: Moona Kanwal. Email:

Computers, Materials & Continua 2024, 78(2), 2419-2440. https://doi.org/10.32604/cmc.2024.047223

Received 30 October 2023; Accepted 18 December 2023; Issue published 27 February 2024

Abstract

The user’s intent to seek online information has been an active area of research in user profiling. User profiling considers user characteristics, behaviors, activities, and preferences to sketch user intentions, interests, and motivations. Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation. The user’s complete online experience in seeking information is a blend of activities such as searching, verifying, and sharing it on social platforms. However, a combination of multiple behaviors in profiling users has yet to be considered. This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition. This research explores information search, verification, and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning. The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation. User feedback is based on online behavior and practices collected by using a survey method. The participants include both males and females from different occupation sectors and different ages. The data collected is subject to feature engineering, and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics. Different techniques are evaluated, and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136. Feature average is computed to identify user intent type characteristics. The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%. This research successfully extracts different user types based on their preferences in online content, platforms, criteria, and frequency. The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.

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Keywords

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Supplementary Material

Supplementary Material File

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