SURVEY ON VARIOUS SOCIAL NETWORKS RANKING MECHANISM

Authors

  • Kajal B. Asari M.Tech Student, Dept. of Computer Engineering, Parul Institute of Engineering and Technology, Parul University, Vadodara, Gujarat, India
  • Mohammed Husain Bohra Asst. Professor, Dept. of Computer Science and Engineering, Parul Institute of Engineering and Technology, Parul University, Vadodara, Gujarat, india

Keywords:

Page Rank, Polarity, Node rank, Reputation, Engagement rate, Social network

Abstract

Social Networks are the common platform for the present generations to share ideas. The members of these sites have grown to billions in the last decade and much more at present. Different social networking services like Facebook, Twitter, YouTube, etc., allow the person to create a public profile and also allow that person to view content posted by others as well as to post their own content or opinions.  In this paper describes survey on a different ranking mechanism of social media such as Page rank, Polarity, Engagement rate, etc. Social networks need to manage and control the drift of insane amount of information by filtering and ranking everything in order to ensure they are right there for users’ viewing pleasure. The objective of this paper is to show what metrics, measurements are used for by researchers to study and analyze social network, in their work with academic purposes. The concern of this paper is to show which data, metrics, and measures are considered as a basis of the analysis.

References

I. Josep M. Pujol, Ramon Sangüesa , Jordi Delgado “Distributed and Adaptable Ranking Algorithm for Reputation and Relevance Extraction."Universitat Politècnica de Catalunya LSI, Jordi Girona Salgado, 1-3 08034 Barcelona, Spain 17 August 2015

II. Athma Ram K., Chandra Sekharaiah K.” Metrics of Social Websites: A Case Study” 978-93-80544-12-2014 IEEE

III. M. Chaudhary, H. Kumar “A Framework to Rank Nodes in Social Media Graph Based on Sentiment-Related Parameters” Proceedings of International Conference on ICT for Sustainable Development, Advances in Intelligent Systems and Computing 409, Springer 2016, 271-279.

IV. ] Sokratis Vavilis, Milan Petković, Nicola Zannone ”A reference model for reputation syste-ms” Decision Support Systems 61 (2014) 147–154

V. Vikas Jindal a, Seema Bawa b, Shalini Batra”A review of ranking approaches for semantic search on Web” Information Processing and Management 50 (2014)

VI. M. Chaudhary, H. Kumar “A Framework to Rank Nodes in Social Media Graph Based on Sentiment-Related Parameters” Proceedings of International Conference on ICT for Sustainable Development, Advances in Intelligent Systems and Computing 409, Springer 2016, 271-279

VII. Homa Baradaran Hashemi, Nasser Yazdani, Azadeh Shakery, Mahdi Pakdaman Naeini Application of Ensemble Models in Web Ranking, 2010 5th International Symposium on Telecommunications (IST'2010) 2010 IEEE

VIII. Walaa Medhat, Ahmed Hassan, Hoda Korashy “Sentiment analysis algorithms and applications: A survey” Ain Shams Engineering Journal (2014) 5, 1093–1113,Elsviear

IX. Vikas Jindal a, Seema Bawa b, Shalini Batra “Digital Brand Management A Study on the Factors Affecting Customers Engagement in Facebook Pages”2015 International confrerence on samart Technologies and mangagment 71-75

X. Page, L., Brin, S., R. Motwani, T. Winograd, The PageRank citation ranking: Bringing order to the Web, submitted for publication.

XI. Klemm, K., and Eguíluz, V.M. Highly clustered scalefree networks. ArXiv:cond mat/0107606, 2001.

XII. Adamic, L.A., and Adar, E. Friends and neighbors on the web, 2000.

XIII. M. Witkowski, A. Artikis, J. Pitt, Experiments in building experiential trust in a society of objective-trust based agents, Trust in Cyber-societies, LNCS, 2246, Springer, 2001, pp. 111–132.

XIV. Liang, B., Liu, Y., Zhang, M., Ma, S., Ru, L., & Zhang, K. (2014). Searching for people to follow in social networks. Journal of Expert Systems with Applications, 41, 7455–7465.

XV. Ortega, F. J., Troyano, J. A., Cruz, F. L., Vallejo, C. G., & Enríquez, F. (2012). Propagation of trust and distrust for the detection of trolls in a social network. Journal of Computer Networks, 56, 2884–2895.

XVI. Read J, Carroll J. Weakly supervised techniques for domainindependent sentiment classification. In: Proceeding of the 1st international CIKM workshop on topic-sentiment analysis for mass opinion; 2009. p. 45–52.

Additional Files

Published

15-05-2017

How to Cite

Kajal B. Asari, & Mohammed Husain Bohra. (2017). SURVEY ON VARIOUS SOCIAL NETWORKS RANKING MECHANISM . International Education and Research Journal (IERJ), 3(5). Retrieved from https://ierj.in/journal/index.php/ierj/article/view/965

Issue

Vol. 3 No. 5 (2017): MAY ISSUE, INTERNATIONAL EDUCATION AND RESEARCH JOURNAL

Section

Articles

License

Copyright (c) 2021 International Education and Research Journal (IERJ)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.