A study of Social Media Data Management using R Programming
Keywords:
Social Media Data Management, R ProgrammingAbstract
Since a regularly expanding piece of the populace influences utilization of web-based social networking in their everyday to lives, web-based Social Media Data Management (SMDM) is being broke down in various orders. The online networking Data Management process includes four particular advances, collection, data discovery,, preparation, and analysis. While there is a lot of literature on the difficulties and challenges including particular data analysis strategies, there scarcely exists inquire about on the phases of information revelation, accumulation, and planning. We propose a system for online social media data management is R.R is an open-source data analysis environment and programming language, allows user to conduct a number of tasks that are essential for the effective processing and analysis of more than 1000 TB of data. R includes different packages which are useful in analysis of data. The process of converting data into knowledge, insight and understanding is Data analysis, which is a critical part of statistics. For the effective processing and analysis of Social media Data, it allows users to conduct a number of tasks that are essential. Because R is a high level language, a function can have a deep hierarchy of operation. Although online social networking data processing might be proficient with different devices too, it is the point at which one stages on to the information examination that R truly stands interesting, outstanding to the huge amount of third-party algorithms and built-in statistical formulae available.
References
[2] Kim, Y., Choi, K. S., & Natali, F. (2016). Extending the Network: The Influence of Offline Friendship on Twitter Network Full papers. Proceedings of the Americas Conference on Information Systems.
[3] Beier, M., & Wagner, K. (2016). Social media adoption: barriers to the strategic use of social media in SMEs. Proceedings of the european conference on information systems
[4] Shen, Y., Hock Chuan, C., & Cheng, S. H. (2016). The Medium Matters: Effects on WhatConsumers Talk about Regarding Movie Trailers. In Proceedings of the International Conference on Information Systems.
[5] Liu, S.-H., Chou, C.-H., & Liao, H.-L. (2015). An exploratory study of product placement in social media. Internet Research, 25(2), 300–316. http://dx.doi.org/10.1108/IntR-12-2013-0267.
[6] Nopparoot Kitcharoen, Sasithorn Kamolsantisuk, Reenapat Angsomboon, and Tiranee Achalakul. “RapidMiner Framework for Manufacturing Data Analysis on the Cloud,”IEEE , 2013.
[7] RStudio Team (2015). RStudio: Integrated Development for R. RStudio, Inc., Boston, MA URL http://www.rstudio.com/
[8] Stefan Stieglitz, Milad Mirbabaie, Björn Ross, Christoph Neuberger[Social media analytics – Challenges in topic discovery, data collection, and data preparation] in International journal of information management.
[9] G. Ostrouchov, W.-C. Chen, D. Schmidt, P. Patel, Programming with big data in R, http://r-pbd.org/, 2012.
[10] Stephen Cass, The 2016 top programming languages, http://spectrum.ieee.org/computing/software/the-2016-top-programming-languages, 2016.
[11] Winston Chang, Profvis: visualize profiling data, R package version 0.1.6, 2016.
[12] W.-C. Chen, G. Ostrouchov, D. Schmidt, P. Patel, H. Yu, pbdMPI: program-ming with big data— interface to MPI, R package, http://cran.r-project.org/package=pbdMPI, 2012.
[13]D. Schmidt, W.-C. Chen, G. Ostrouchov, P. Patel, pbdBASE: programming with big data— core pbd classes and methods, R package, http://cran.r-project.org/package=pbdBASE, 2012.
