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Big Data Mining and Analytics  2020, Vol. 3 Issue (4): 259-279    DOI: 10.26599/BDMA.2020.9020006
Survey on Data Analysis in Social Media: A Practical Application Aspect
Qixuan Hou(),Meng Han*(),Zhipeng Cai*()
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.

Key wordssocial media      topic analysis      time series analysis      sentiment analysis      network analysis      disaster management      bio-surveillance      business intelligence     
Received: 21 May 2020      Published: 07 December 2020
Corresponding Authors: Meng Han,Zhipeng Cai     E-mail:;;
About author: Qixuan Hou is a master student in analytics at Georgia Institute of Technology. She received the BS degree from Georgia Institute of Technology in 2019. Her research interests are in the area of data science, with experience executing data-driven solutions.|Meng Han is an assistant professor at the College of Computing and Software Engineering, Kennesaw State University. He received the PhD degree in computer science from Georgia State University in 2017. He is currently an IEEE member and an IEEE COMSOC member. He has the unique research experiences built upon big data, cyber data, and social data with academic achievements of 6 book chapters/books, more than 20 first-authored and more than 20 co-authored publications in international journals and conferences, with 4 best paper awards and 2 best paper runner-up awards. His research interests include data-driven intelligence, AI security & privacy, and blockchain technologies.|Zhipeng Cai received the MS and PhD degrees from University of Alberta in 2004 and 2008, respectively. He is currently an associate professor at the Department of Computer Science, Georgia State University (GSU). Prior to joining GSU, he was a research faculty at the School of Electrical and Computer Engineering, Georgia Institute of Technology. His research areas focus on networking, big data, data security, and artificial intelligence. He is the recipient of an NSF CAREER Award.
Cite this article:

Qixuan Hou,Meng Han,Zhipeng Cai. 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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Fig. 1 Social media’s categories and their examples.
Fig. 2 Number of social media users in the world.
Fig. 3 Number of users using social media platforms from 2002 to 2019.
Fig. 4 Four challenges, four analysis techniques, and three impacts of social media-based applications.
Fig. 5 Common application pipeline.
2010Cataldi et al.[18]QueriesCreate a navigable topic graph with emerging topics over time
2010Sizov[19]Topic modelingGeoFolk to discover latent topics
2010Song et al.[20]Topic modelingExplore spatio-temporal framework for related topic search
2012Han and Kang[21]QueriesIdentify the personalized relevance of social issues to targets
2012Song et al.[22]Topic modelingIdentify the personalized relevance of social issues to targets
2012Hu et al.[23]Topic modelingPropose a topic modeling with user features
2013Kamath et al.[24]HashtagsStudy the spatio-temporal dynamics of Twitter hashtags
2013Ma et al.[25]Topic modelingPropose Tag-Latent Dirichlet Allocation (TLDA) to bridge hash tags and topics
2013Bogdanov et al.[26]Topic modelingIdentify the personalized relevance of social issues to targets
2015Jang and Myaeng[27]Topic modelingAnalyze spatially oriented topic versatility
2015Yao et al.[28]Topic modelingAnalyze news trends in Twitter
2016Qian et al.[29]Topic modelingAnalyze multi-model event topic model
2016Musaev and Hou[30]QueriesDetect landslides with Twitter data
2016Rohani et al.[31]Topic modelingExplore an unsupervised topic modeling approach
2018Argyrou et al.[32]HashtagsPrepare training images for Automatic Image Annotation (AIA)
2018Ejaz et al.[33]Topic modelingAnalyze news using ontology
Table 1 Summary of previous works in topic analysis.
2007Fu et al.[34]Visualization toolVisualize consumer-generated media
2010Mathioudakis and Koudas[35]Visualization toolTwitterMonitor: Trend detection
2011Yang and Leskoves[36]ClusteringDetect patterns of temporal variation
2012Zhao et al.[37]SmoothingIdentify event-related bursts
2012Chae et al.[38]Seasonal-trend decompositionDetect abnormal event
2014Ahn and Spangler[39]PredictionPredict sales
2014Kucher et al.[40]Visualization toolVisualize stance markers in online social media
2015Healy et al.[41]Peak detectionDetect events
2015Tsuboi et al.[42]PredictionPredict product purchases
2015Nusratullah et al.[43]Analyze varianceDetect changes in email notwork
2015Johnson and Ni[44]PredictionInfer dynamic consumer valuations
2015Zhao et al.[45]PredictionPredict the reposting number of micro-blog messages
2017Ni et al.[46]PredictionForecast the subway passenger flow
2017Dahouei[47]SmoothingIdentify hot topics
2017Comito et al.[48]Peak detectionIdentify deviation from user normal behavior
Table 2 Summary of previous works in time series analysis.
2012Rui and Whinston[49]Learning-basedDesign business intelligence system
2013Yuan et al.[50]Lexicon-based & learning-basedSentiment classify Chinese micorblogs
2013Yu et al.[51]Learning-basedAnalyze firm equity value
2014Wang et al.[52]Learning-basedEnhance sentiment analysis technique
2016Wang et al.[53]Lexicon-basedBuild finer-grained sentiment analysis
Table 3 Summary of previous works in sentiment analysis.
2003Popescul and Ungar[54]ClassificationPredict links
2007Noweell and Kleinberg[55]ClassificationPredict links
2010Cataldi et al.[18]Page rankDetect emerging topics
2011Rossetti et al.[56]ClassificationPredict links
2012Michalski et al.[57]Time seriesPredict links
2013Smith[58]VisualizationNodeXl: Visualizae network
2016Musaev and Hou[30]Page rankDetect landslides with Twitter data
2016Erlandsson et al.[59]Association rule learningIdentify influencers
2016Li et al.[60]Location-based social networksIdentify influencers
2017Zhao et al.[61]Degree/betweenness/closeness centralityIdentify influencers
2019Tang et al.[62]The second-order Independent Cascade (IC) modelIdentify influencers
Table 4 Summary of previous works in network analysis.
Fig. 6 Most used languages on Twitter in September 2013.
Fig. 7 Cellular generation evolution.
Fig. 8 Global mobile video traffic from 2017 to 2022[120].
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