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Big Data Analytics in Social Media: A Triple T (Types, Techniques, and Taxonomy) Study

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ITNG 2021 18th International Conference on Information Technology-New Generations

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1346))

Abstract

Society 2.0; with the help of recent advancements in the internet and web 2.0 technology, makes the social media-based platform the most popular source for big data research. Big Data Analytics contributes by adjusting, analyzing, and forecasting insightful recommendations from this huge source of noisy & mostly unstructured “Big Social Data”. We present 10 mostly used big data analytics in the working domain of social media-based platforms. Different popular techniques or algorithms related to each big data analytic are also listed in this study. We show that “Text Analytics” is the most popular big data analytics in social media data analysis. Through this research, we try to explain the 10 Bigs of big data and introduce the “Sunflower Model of Big Data”. We also explain the reason why the social media-based platform is so significant and popular source of big data by analyzing the most recent statistics. This study will be a handful for all other researchers who want to work with big data in social media and in advance; make their work easy for selecting the best big data analytics method suitable for their research work.

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Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND, USA

    Md. Saifur Rahman & Hassan Reza

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  1. Md. Saifur Rahman
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  2. Hassan Reza
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Correspondence to Md. Saifur Rahman .

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  1. Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, NV, USA

    Shahram Latifi

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Rahman, M.S., Reza, H. (2021). Big Data Analytics in Social Media: A Triple T (Types, Techniques, and Taxonomy) Study. In: Latifi, S. (eds) ITNG 2021 18th International Conference on Information Technology-New Generations. Advances in Intelligent Systems and Computing, vol 1346. Springer, Cham. https://doi.org/10.1007/978-3-030-70416-2_62

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  • DOI: https://doi.org/10.1007/978-3-030-70416-2_62

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-70415-5

  • Online ISBN: 978-3-030-70416-2

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