Authors : Shaik. Allabhakshu; Manam Om Rupesh; Kodela Jayasri; Thungaturthi Satya Sai Himaja; Katikam Mahesh

Volume/Issue : Volume 10 - 2025, Issue 4 - April

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DOI : https://doi.org/10.38124/ijisrt/25apr1128

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Abstract : In today’s digital age, understanding how users interact with online platforms has become more important than ever, especially for reshape experiences and protect security. This project introduces an innovative approach to analyzing and classifying user behavior by using machine learning, with a focus on predicting information-seeking patterns based on social media and locating data. Inspired by real world needs, we developed a system that uses a fine-tuned Random Forest Classifier to categorize user activities into "uncertain Behavior, Good Behavior, or Neutral Behavior using features like gender, age, location latitude and longitude, and social metrics such as followers, friends, favorites, and statuses. The model does a great job reaching an impressive accuracy of 90.21%. What makes this project special is its interactive edge we built a user friendly interface using Jupyter allowing anyone to input their own data think of it like filling out a digital profile and get instant predictions about their behavior type. It is for marketer wanting to personalize ads, security teams detecting possible risk, or researcher studying online habits, this tool delivers action able insights with a simple click. The system also save predictions to a CSV file for future reference and offers a peek into advanced possibilitie with plans for real time deployment using Flask and Drawing from established research on user direction and machine learning, this project balances technical culture with practical usability aiming to enhance our understanding of digital behavior while keeping privacy and ethics in mind. It a step toward smarter more natural online environments crafted with care and Interest.

Keywords : Machine learning, User behaviors, Random Forest Classifier, Accuracy, Interactive interface, Privacy, Social media, Information-seeking patterns, Real-time deployment, User-friendly interface , Security K. Jayasri, SK. AllaBhakshu, M. Om Rupesh, T. SatyaSaiHimaja, K. Mahesh, 2025, Machine Learning Approaches to classification of online users by exploiting information seeking behaviour.

References :

1. L.S.Vygotsky,MindinSociety:TheDevelopment of Higher Psychological Processes. Cambridge,M A,USA:HarvardUniv.Press,1978.
2. K.A.Mills,‘‘Shrekmeetsvygotsky:Rethinking adolescents’ multimodal literacy practices in schools,’’J.AdolescentAdultLiteracy,vol.54,no.1, pp.35–45,Sep.2010.
3. A. Halevy, C. Canton-Ferrer, H. Ma, U. Ozertem, P. Pantel, M. Saeidi, F. Silvestri, and V. Stoyanov,‘‘ Preservingintegrityinonlinesocial networks,’’ Commun. ACM, vol. 65, no. 2, pp. 92–98,Feb.2022.
4. X.Feng, X. Wang, and Y. Zhang, ‘‘Research on the effect evaluation and the time-series evolution ofpublic culture’s Internet communication under the background of new media: Taking theinformation disseminationofred tourism culture as an example,’’ J. Comput. Cultural Heritage,vol.16,no.1,pp.1–15,Mar.2023.
5. C. I. Eke, A. A. Norman, L. Shuib, and H. F. Nweke, ‘‘A survey of user profiling: State-of-the art,challe nges,andsolutions,’’IEEEAccess,vol.7, pp.144907–144924,2019.
6. J. Liu, M. Mitsui, N. J. Belkin, and C. Shah, ‘‘Task, information seeking intentions, and user behavior: Toward a multi-level understanding of web search,’’ in Proc. Conf. Human Inf. Interact. Retr.,Glasgo w,U.K.,Mar.2019,pp.123–132.
7. J. L. Hale, B.J.Householder, andK. L. Greene, ‘‘The theory of reasoned action,’’ in The Persuasion Handbook:Developmentsin Theory and Practice, vol. 14. Newbury Park, CA, USA: Sage,2002,pp.259–286.
8. J. Shi, P. Hu, K. K. Lai, and G. Chen, ‘‘Determinants of users’ information dissemination behavioron socialnetworkingsites:Anelaboration likelihood model perspective,’’ Internet Res., vol.28,no. 2,pp.393–418,Apr.2018.
9. P.Bedi,S.B.Goyal,A.S.Rajawat,R.N.Shaw,and A.Ghosh,‘‘Aframeworkfor personalizing atypical web search sessions with concept-based user profiles using selective machine learning techniques,’’ in Advanced Computing and Intelligent Technologies (Lecture Notes in Networks andSystems),vol.21 8.Singapore:Springe,2022.52
10. M.Soleymani,M.Riegler,andP.Halvorsen,‘‘Multimodal analysis of user behavior and browsed            content  under different   imagesearch intents,’’ Int. J. Multimedia Inf. Retr., vol. 7, no. 1, pp.29–41,Mar.2018,doi:10.1007/s13735-018-0150-6.
11. D. Koehn, S. Lessmann, and M. Schaal, ‘‘Predicting online shopping behaviour from Click stream data using deeplearning, ’’ExpertSyst.  Appl., vol. 150, Jul. 2020, Art. no. 113342.
12. H.Yoganarasimhan,‘‘Search personalization using machine learning,’’ Manage. Sci., vol. 66, no.3,pp.1045–1070,Mar.2020.
13. T.Ruotsalo,J.Peltonen,M.J.Eugster,D.Głowacka, P.Floreen,P.Myllymaki,G.Jacucci, andS.Kaski,‘‘Interactiveintentmodelingfor exploratory search,’’ ACM Trans. Inf. Syst., vol. 36,no.4, p.44,Oct.2018,doi:10.1145/3231593.
14. S.K.Shivakumar,‘‘Asurveyandtaxonomy ofintent- based code search,’’ Int. J. Softw. Innov., vol.9,no.1, pp.69–110,Jan. 2021.
15. P.Ren,Z.Liu,X.Song,H.Tian,Z.Chen,Z.Ren, and M. de Rijke, ‘‘Wizard of search engine: Accesstoinformationthroughconversationswith search engines,’’ in Proc. 44th Int. ACM SIGIR Conf.Res. Develop.Inf.Retr.,Jul.2021.