Machine learning approach to classification of online users by exploiting information seeking behavior

K.V.Srinivasa Rao; Pakala Yedukondalu

Author(s)

K.V.Srinivasa Rao Dept of HOD of CSE- Cyber Security, Prakasam Engineering College, India

Pakala Yedukondalu Dept of Computer Science and Engineering, Prakasam Engineering College, India

Abstract

The internet is overflowing with unfiltered, impromptu, and continuous material from many sources due to technology in today's environment. In order to successfully deliver information depending on user intent, complex algorithms are created. Users' online experiences include a number of information-seeking activities, such as sharing, searching, and information verification. However, a thorough investigation of this complex user behavior is still pending. This study helps to identify different types of users based on their online engagement, propose a user intent-machine learning model for classifying users based on their online search, share, and verification behavior, and show that dynamic online interactions can be categorized based on their searching, sharing, and verifying behavior. Participants from a wide range of age, gender, and vocational backgrounds complete a questionnaire designed to collect user input on online behavior and practices. After a thorough feature engineering process, K-Mean clustering is used to discover user intent classes or profiles and their attributes based on the key features. To identify these classes, data is subsequently used to train a supervised learning Linear Discriminant Analysis Classifier (LDAC). With 80% accuracy, the suggested framework was able to predict the user intent class. A second user study collects data on users' dynamic interactions, which is used to test the model further. The user profiles that emerge from clustering are used by human raters to label the information search, exchange, and verify activity data after it has been converted to suit the model. While the model predicted the user with 67% accuracy, the study obtains an Inter-rater reliability (IRR) of 60%. According to this study, a user's motivation for looking for information, their propensity to share information on social media, and their propensity to believe information to be reliable can all help to understand their intentions, spot behavioral patterns, and identify intent through dynamic interactions that can be utilized for search engine optimization and targeted marketing

Publication Details

Page(s) 99-107

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 5, Year: 2024

Request the full text from PASTIC

Keywords

Information Sharing, Cluster, Human Behavior, browsing preference, user behavior, search reasons, User intent

References

. .These findings can be explored further to tailor online services to use generic user profiles that can be securely used to increase the user experience by providing the information the user generally intends. The research uses user online interaction and activities on the UIC model to test its efficacy. Human raters annotate the interaction data and LDA model, and Inter-rater reliability (IRR) is computed to validate the annotation, User profile, and UIC model prediction. The experts accepted the IRR of 60% and successfully validated the model performance in predicting users on dynamic data. The research contributes to the development of a framework for profiling users based on unique three-dimensional data, i.e., user search intent, information dissemination needs, and content credibility criteria. These aspects of user behavior have been researched separately but not as a component of user intent. In this digital era, where information is accessed from all online platforms, information integrity is an important aspect. The research shows a correlation between search and share, whereas verification adds weight to the user's truth-seeking desire. Insights from this study can help in better design of search filters, Search Engine Optimization (SEO), targeted advertising, and shopping experience based on user profiling. , : .

Vygotsky L. S. .1978 ."Mind in society: The development of higher psychological processes. , : .

M. K. K. .2010 ."Shrek meets Vygotsky. Rethinking adolescents multimodel literacy prac-tices in schools. " Journal of Adolescent & Adult Literacy, 54(1) : 35-45.

Halevy A.,Canton-Ferrer C.,Ma H.,Pantel P.,Saeidi M.,Silvestri F.,Stoyanov V. .2022 ."Preserving integrity in online social networks,". Communications of the ACM, 65(2) : 92-98.

Feng X.,Wang X.,Zhang Y. .2022 ."Research on the Effect Evaluation and the Time Series Evolution of Public Culture's Internet Communication Under the Background of New Media: Taking the Information Dissemination of Red Tourism Culture as an Example.,". ACM Journal on Computing and Cultural Heritage, : .

Eke C. I.,Norman A. A.,Shuib L.,Nweke H. F. .2019 ."A survey of user profiling: State-of-the-art, challenges, and solutions,". IEEE Access, 7 : 144907-144924.

Liu J.,Mitsui M.,Belkin N. J.,Shah C. .2019 ."Task, information seeking intentions, and user behavior: Toward a multi-level understanding of Web search," in In Proceedings of the 2019 conference on human information interaction and retrieval. , : .

Hale J. L.,Householder B. J.,Greene K. L. .2002 ."The theory of reasoned action," in The persuasion handbook: Developments in theory and practice. , 14 : 259-286.

Shi J.,Hu P.,Lai K.,Chen G. .2018 ."Determinants of users' information dissemination behavior on social networking sites: An elaboration likelihood model perspective,". Internet Research, 28(2) : 393-418.

Bedi P.,Goyal S. B.,Rajawat A. S.,Shaw R. N.,Ghosh A. .2022 ."A Framework for Personalizing Atypical Web Search Sessions with Concept-Based User Profiles Using Selective Machine Learning Techniques. " Advanced Computing and Intelligent Technologies. Lecture Notes in Networks and Systems, 218 : .

Soleymani M.,Riegler M.,Halvorsen P. . ."Multimodal analysis of user behavior and browsed content under different image search intents,". International Journal of Multimedia Information Retrieval, : .

Koehn D.,Lessmann S.,Schaal M. .2020 ."Predicting online shopping behaviour from clickstream data using deep learning, ". Expert Systems with Applications, 150 : .

Yoganarasimhan H. .2020 ."Search personalization using machine learning,". Management Science, 66(3) : 1045-1070.

Ruotsalo T.,Peltonen J.,Eugster M. J.,Głowacka D.,Floreen P.,Myllymaki P.,Jacucci G.,Kaski S.,Oct S. .2018 ."Interactive Intent Modeling for Exploratory Search,". ACM Trans. Inf, 36(4-44) : 46.

Shivakumar S. K. .2021 ."A Survey and Taxonomy of Intent-Based Code Search.,". International Journal of Software Innovation (IJSI), 9(1) : 69-110.