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A new cluster computing technique for social media data analysis

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Abstract

Social media data analysis has emerged as an important part of the new generation of information technology. However, Social media data analysis is based on Data Mining. A new algorithm based on \(\hbox {M}^{\mathrm{x}}/\hbox {G}/1\) Queue model is proposed to calculate money flow of Social media stocks. The proposed algorithm exploits the transaction data behind the social media stocks, such as volume, the Commission transaction queue, closing price, etc. Through the Social media stock trading data, we use Matlab programming to carry out data mining and calculation of money flows, and draw the money inflow and outflow curve. The experimental results show that our new model has strong practicability for the data mining of Social media stocks. The experimental results can also reflect the development of the social media more clearly. The method of data mining on the Social media stocks provides an indirect way to study the economic and regional economy of the Social media stocks.

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Acknowledgements

The Project was supported by the National Natural Science Foundation of China (No. 61402185), Natural Science Foundation of Guangdong Province (No. 2015A030313382), and Guangdong Provincial Public Research and Capacity Building Foundation funded project (Nos. 2016A020223012, 2015A020217011).

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  1. School of Computer, South China Normal University, Guangzhou, 510631, China

    Qingzhen Xu & Miao Li

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  1. Qingzhen Xu
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  2. Miao Li
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Correspondence to Qingzhen Xu.

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Xu, Q., Li, M. A new cluster computing technique for social media data analysis. Cluster Comput 22 (Suppl 2), 2731–2738 (2019). https://doi.org/10.1007/s10586-017-1436-9

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