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Open Access

Survey on Data Analysis in Social Media: A Practical Application Aspect

College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA
Data-driven Intelligence Research (DIR) Lab of College of Computing and Software Engineering, Kennesaw State University, Kennesaw, GA 30060, USA
Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA
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Social media has more than three billion users sharing events, comments, and feelings throughout the world. It serves as a critical information source with large volumes, high velocity, and a wide variety of data. The previous studies on information spreading, relationship analyzing, and individual modeling, etc., have been heavily conducted to explore the tremendous social and commercial values of social media data. This survey studies the previous literature and the existing applications from a practical perspective. We outline a commonly used pipeline in building social media-based applications and focus on discussing available analysis techniques, such as topic analysis, time series analysis, sentiment analysis, and network analysis. After that, we present the impacts of such applications in three different areas, including disaster management, healthcare, and business. Finally, we list existing challenges and suggest promising future research directions in terms of data privacy, 5G wireless network, and multilingual support.


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Big Data Mining and Analytics
Pages 259-279
Cite this article:
Hou Q, Han M, Cai Z. Survey on Data Analysis in Social Media: A Practical Application Aspect. Big Data Mining and Analytics, 2020, 3(4): 259-279.








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Received: 21 May 2020
Accepted: 08 June 2020
Published: 16 November 2020
© The authors 2020

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