Time Series Predictive Models for Social Networking Media Usage Data: The Pragmatics and Projections

Main Article Content

M. A. Jayaram
Gayitri Jayatheertha
Ritu Rajpurohit

Abstract

Aims: We have set forth three main objectives in the work presented in this paper, they are namely, to study how social networking media usage is surging over the time for three social media networks viz., Facebook, Twitter and LinkedIn, ii.to develop best fitting time series predictive models for predicting future usage of three network media  and, iii. to make a comparative analysis to herald the ups and downs noticed in the usage across three network media considered.

Study Design: Application of time series techniques for the analysis of social network user’s data. The main research question addressed by this work is to see how time series models augurs for time dependent data such as the one chosen in this research.

Place and Duration of Study: Research Center, Department of Master of Computer Applications, Siddaganga Institute of Technology, Tumakuru, Karnataka, India, between January 2020- April 2020.

Methodology: The work delved on collection three social network users (Facebook, LinkedIn, and Twitter) data for a span of nine years i.e., for the tenure 2011-2019. One dimensional, two dimensional and three dimensional visual analytics is made prior to time series analysis. Time series predictive analytics involved development of best fits for prediction. To select the best fits among linear, polynomial, exponential, power function and logarithmic models, mean absolute error and root mean square error metrics were used.

Results: Linear, polynomial function trend lines proved to be the best for Facebook, LinkedIn and Twitter respectively with low values of MAE and RMSE and high values of regression coefficients as compared with other kinds of models. Apart from the error metrics, the Theil’s U-statistic values of 0.928, 1.008 and 1.21 for Facebook, Twitter and LinkedIn also heralded the fact that these functions are superior models when compared with other naïve models. It is also projected that by 2025, Facebook will see 10,000 billion, followed by LinkedIn at 1500 billion while Twitter would see 750 billion people if same kind of surge trend prevails in user numbers across three networks considered in this research.

Conclusion: This paper presented a unique work which is supposedly deemed to be the first of its kind to the best of the knowledge of authors. The models come with a limitation that, they can provide accurate projection if the same trend prevails in the pattern of upheavals in usage.

Keywords:
Social media networks, Facebook, Linkedin, Twitter, time series models, trend analysis

Article Details

How to Cite
Jayaram, M. A., Jayatheertha, G., & Rajpurohit, R. (2020). Time Series Predictive Models for Social Networking Media Usage Data: The Pragmatics and Projections. Asian Journal of Research in Computer Science, 6(1), 37-54. https://doi.org/10.9734/ajrcos/2020/v6i130151
Section
Original Research Article

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