Social Media Analytics using Data Mining

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Abstract

There is a rapid increase in the usage of social media in the most recent decade. Getting to social media platforms for example, Twitter, Facebook LinkedIn and Google+ via mediums like web and the web 2.0 has become the most convenient way for users. Individuals are turning out to be more inspired by and depending on such platforms for data, news and thoughts of different clients on various topics. The substantial dependence on these social platforms causes them to produce huge information described by three computational issues in particular; volume, velocity and dynamism. These issues frequently make informal organization information exceptionally complex to break down physically, bringing about the related utilization of computational method for dissecting them.

References

15 Cites in Article
  1. Stephen Borgatti,Martin Everett (2006). A Graph-theoretic perspective on centrality.
  2. R Burt (2005). Brokerage and closure: An introduction to social capital.
  3. Rumi Ghosh,Kristina Lerman (2011). Parameterized centrality metric for network analysis.
  4. J Scott (2011). Social network analysis: developments, advances, and prospects.
  5. Charu Aggarwal (2011). An Introduction to Social Network Data Analytics.
  6. S Fortunato (2010). Community detection in graphs.
  7. M Girvan,M Newman (2002). Community structure in social and biological networks.
  8. M Newman (2010). Networks: An introduction.
  9. S Papadopoulos,Y Kompatsiaris,A Vakali,P Spyridonos (2012). Community detection in socialmedia Data Mining and Knowledge Discovery.
  10. R Burke (2002). Hybrid recommender systems: Survey and Experiments.
  11. F Liu,H Lee (2010). Use of social network information to enhance collaborative filtering performance.
  12. M Pham,Y Cao,R Klamma,M Jarke (2011). A clustering approach for collaborative filtering recommendation using social network analysis.
  13. D Murthy,A Gross,A Takata,S Bond (2013). Evaluation and Development of Data Mining Tools for Social Network Analysis.
  14. Xiang Ruan,Xiong Hu,Xia Zhang (2014). Research on Application Model of Semantic Web-Based Social Network Analysis.
  15. L Zhou,L Ding,T Finin (2011). How is the semantic web evolving? A dynamic social network perspective.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Hibatullah Alzahrani. 2016. "Social Media Analytics using Data Mining". Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 16 (GJCST Volume 16 Issue C4).

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords
Classification
GJCST-C Classification H.2.8
K.4.2
Version of record

v1.2

Issue date
November 6, 2016

Language
English
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Social Media Analytics using Data Mining

Hibatullah Alzahrani
Hibatullah Alzahrani Saudi Arabian Cultural Mission