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User Activity Classification and Domain-Wise Ranking Through Social Interactions

User Activity Classification and Domain-Wise Ranking Through Social Interactions

Ravindra Kumar Singh, Harsh Kumar Verma
Copyright: © 2022 |Volume: 11 |Issue: 2 |Pages: 15
ISSN: 2160-9772|EISSN: 2160-9799|EISBN13: 9781799898245|DOI: 10.4018/IJSDA.20220701.oa5
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MLA

Singh, Ravindra Kumar, and Harsh Kumar Verma. "User Activity Classification and Domain-Wise Ranking Through Social Interactions." IJSDA vol.11, no.2 2022: pp.1-15. http://doi.org/10.4018/IJSDA.20220701.oa5

APA

Singh, R. K. & Verma, H. K. (2022). User Activity Classification and Domain-Wise Ranking Through Social Interactions. International Journal of System Dynamics Applications (IJSDA), 11(2), 1-15. http://doi.org/10.4018/IJSDA.20220701.oa5

Chicago

Singh, Ravindra Kumar, and Harsh Kumar Verma. "User Activity Classification and Domain-Wise Ranking Through Social Interactions," International Journal of System Dynamics Applications (IJSDA) 11, no.2: 1-15. http://doi.org/10.4018/IJSDA.20220701.oa5

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Abstract

Twitter has gained a significant prevalence among the users across the numerous domains, in the majority of the countries, and among different age groups. It servers a real-time micro-blogging service for communication and opinion sharing. Twitter is sharing its data for research and study purposes by exposing open APIs that make it the most suitable source of data for social media analytics. Applying data mining and machine learning techniques on tweets is gaining more and more interest. The most prominent enigma in social media analytics is to automatically identify and rank influencers. This research is aimed to detect the user's topics of interest in social media and rank them based on specific topics, domains, etc. Few hybrid parameters are also distinguished in this research based on the post's content, post’s metadata, user’s profile, and user's network feature to capture different aspects of being influential and used in the ranking algorithm. Results concluded that the proposed approach is well effective in both the classification and ranking of individuals in a cluster.