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Open Access

A Systematic Review Towards Big Data Analytics in Social Media

School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND 58202, USA
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Abstract

The recent advancement in internet 2.0 creates a scope to connect people worldwide using society 2.0 and web 2.0 technologies. This new era allows the consumer to directly connect with other individuals, business corporations, and the government. People are open to sharing opinions, views, and ideas on any topic in different formats out loud. This creates the opportunity to make the "Big Social Data" handy by implementing machine learning approaches and social data analytics. This study offers an overview of recent works in social media, data science, and machine learning to gain a wide perspective on social media big data analytics. We explain why social media data are significant elements of the improved data-driven decision-making process. We propose and build the "Sunflower Model of Big Data" to define big data and bring it up to date with technology by combining 5 V’s and 10 Bigs. We discover the top ten social data analytics to work in the domain of social media platforms. A comprehensive list of relevant statistical/machine learning methods to implement each of these big data analytics is discussed in this work. "Text Analytics" is the most used analytics in social data analysis to date. We create a taxonomy on social media analytics to meet the need and provide a clear understanding. Tools, techniques, and supporting data type are also discussed in this research work. As a result, researchers will have an easier time deciding which social data analytics would best suit their needs.

Keywords

big datasocial mediabig data analyticssocial media analyticstext analyticsimage analyticsaudio analyticsvideo analyticspredictive analyticsdescriptive analyticsprescriptive analyticsdiagnostic analytics

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Big Data Mining and Analytics
Volume 5 Issue 3,
September 2022
Pages 228-244
DOI: 10.26599/BDMA.2022.9020009
Cite this article:
Rahman MS, Reza H. A Systematic Review Towards Big Data Analytics in Social Media. Big Data Mining and Analytics, 2022, 5(3): 228-244. https://doi.org/10.26599/BDMA.2022.9020009
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10.26599/BDMA.2022.9020009.T00110 Bigs including 5 V’s in big data.

Bigs/V’s in big dataMeaningRemark
Big volumeThis indicates the dataset’s size, which is commonly measured in terabytes (TB), petabytes (PB), and other units[14]. Data volume is relative in this case, and it varies depending on a lot of things. Because storage capabilities are rising, even larger datasets will be gathered in the near future; what is described as big data now may not fulfill the barrier tomorrow[3].1st VFundamental characteristics
Big velocityData processing speed is data velocity. The rate at which data are produced and assessed referred to as velocity[22]. It has to do with data latency and throughput[14].2nd V
Big varietyThis refers to the wide range of data kinds, formats, and sources available. Big data can be structured, semi-structured, or unstructured, but it is mostly unstructured in practice. According to statistics, 80 percent of today’s data are unstructured[14,22]. Because social media big data are a mix of nationalities in a culturally diverse, multi-language setting, it is not structured data[14].3rd V
Big veracityIt must be accurate and genuine to be deemed big data. This relates to the data’s trustworthiness[22]. When working with huge data, there is confusion, imperfection, and inconsistency. However, data analytics must be used to extract meaningful insight from ambiguous, partial, and unclear big data sets[14].4th V
Big intelligenceA collection of concepts, methods, and tools for managing and processing large amounts of data automatically and artificially is known as big intelligence[14]. This is a part of big computing and combined computer science, electrical engineering, mechanical engineering, data science, statistics, and so on.Technical characteristics
Big analyticsThis is a combination of algorithms/techniques that support data management, gathering, and data analysis. Analytics uses artificial intelligence and machine learning to extract meaningful pattern which is automatic and reliable[14]. Big analytics can discover hidden patterns from unreadable raw data. Most of the time, big analytics strongly related and used big intelligence for implementation.
Big infrastructureThe architecture, tools, methods, platforms, and services that provide big data processing are referred to as big data infrastructure. The Apache Hadoop ecosystem, distributed data center, supercomputing machine, etc., are critical components of large-scale infrastructure[14].
Big serviceA comprehensive platform capable of serving millions of individuals. Amazon web services, Google cloud services, mobile services, and social networking services are big services[14]. Often these services provide their own Application Program Interfaces (APIs) to get public access.Socio-economic characteristics
Big valueThe aim of a dataset necessitates its relevance and aspect. This implies that big data bring big social value. Big data have revolutionized society in terms of socializing and perceiving, according to its high social worth[14].5th V
Big marketA data-driven market is required. The big market operates at a socioeconomic level[14]. This includes government, defense, education, manufacturing, business, healthcare, finance and insurance, social networking, and more.

10.26599/BDMA.2022.9020009.T002Social media types (purpose-based).

TypeSocial media example
Social networkingFacebook, Myspace[28], Instagram, and VKontakte (VK)[29]
MultimediaYouTube[28], Vimeo, and Vine[29]
Knowledge aggregationWikipedia[28] and Classmates[29]
MicrobloggingTwitter[28], Thumblr, and Sina Weibo[29]
Instant messagingWhatsApp, Messenger, Tencent QQ, WeChat, Viber, Line, and Snapchat[29]
ProfessionalLinkedIn, Viadeo, and Xing[29]
Forum basedBaidu Tieba (known as Postbar internationally)[29]
CommunicationSkype, Hangout, and YY[29]
Social bookmarkingPinterest[29], BibSonomy, CiteULike, Plurk[30], Delicious, Digg, and StumbeUpon[31]
Content votingReddit[29] and Ranker.com
Search and discoveryGoogle and Foursquare[29]
Friendship and datingTagged and Badoo[29]
Music-focusedMyspace[29]
Travel and lifestyleWayn[29] and TripAdvisor[31]
ResearchResearchGate[29] and Google Scholar
Blog hostingWordPress[31]
Opinion and unsolicited reviewsGlassdoor, Zagat, and Yelp[31]
Interest-basedGoogle+[28], beinggirl.com[31], justmommis.com, alphamom.com, and minti.com[32]

10.26599/BDMA.2022.9020009.T003Statistic of active users in social media.

Social mediaNumber of active users (monthly)
Facebook2.375 billion
YouTube1.9 billion
WhatsApp1.6 billion
Instagram1 billion
WeChat1 billion
LinkedIn610 million
Weibo600 million
Twitter330 million
Pinterest265 million
Snapchat190 million
Airbnb150 million
Google+111 million
Flickr90 million
4chan22 million
ResearchGate17 million[36]
Myspace15 million
Periscope10 million

Note: Table 3 shows numbers from 13 June 2019, except ResearchGate which is October 2021.

10.26599/BDMA.2022.9020009.T004Statistics of seven well known social media sites.

Social mediaIndividual statistics
Facebook(1) Monthly consumers: 2.375 billion.
(2) Facebook is used by 69 percent of all persons in the United States.
(3) Every day, 74 percent of users visit this site.
(4) On average, a Facebook consumer spends 35 min per day on the site.
(5) Facebook has 60 million daily active merchant pages.
(6) Facebook has 5 million active marketers.
Twitter(1) Monthly consumers: 330 million.
(2) Within 24 h, 500 million Tweets were sent, with 6000 Tweets being sent in less than 1 s.
(3) Twitter is used by 22 percent of all persons in the United States.
(4) Companies in the United States have begun to invest in digital marketing, with 65.8 percent of those    with 100 or more employees using Twitter.
(5) According to a recent study, 77 percent of Twitter consumers feel positive about a brand on receiving comments on their Tweets.
YouTube(1) Monthly consumers: 1.9 billion.
(2) In less than 1 min, 300 h of content were posted to YouTube.
(3) Each person consumes 40 min of YouTube videos per day on average.
(4) YouTube is consumed by 90 percent of Americans between the ages of 18 and 24.
(5) YouTube is used by 9 percent of small businesses in the United States.
Pinterest(1) Monthly consumers: 265 million.
(2) The platform is used by 28 percent of all Americans.
(3) The male audience increased by 41 percent in 2014, and the number of hours spent on Pinterest tripled to over 75 min by each visitor.
Instagram(1) Monthly consumers: 1 billion.
(2) Every day, about 95 million photographs are shared.
(3) As of now, over 40 billion pictures have been posted.
(4) 37 percent of all persons in the United States use Instagram.
(5) Each Instagram user occupies 15 min a day on the platform.
LinkedIn(1) Monthly consumers: 610 million.
(2) The platform is used by 27 percent of adults in the United States.
(3) LinkedIn profiles have been created by over 3 million businesses.
(4) LinkedIn is used by 17 percent of smaller businesses in the United States.
Snapchat(1) Monthly consumers: 190 million.
(2) Snapchat is used by 24 percent of adults in the United States.
(3) People spend an average of 25 min per day on this platform.
(4) The network is used by 73 percent of Americans aged between 18 to 24 years.

Note: Table 4 shows numbers from June 13, 2019.

10.26599/BDMA.2022.9020009.T005Number of articles per database.

Source databaseNumber of papers
ACM digital library (ACM)502
IEEE Xplore (IEEE)375
ScienceDirect-Elsevier (SD)625

10.26599/BDMA.2022.9020009.T006Most used social data analytics.

Study sourceTitle of the paperAuthor (Publication year)Discussed BDAs
S1Beyond the hype: Big data concepts, methods, and analytics[3]Gandomi and Haider (2015)(1) Text analytics (text mining); (2) audio analytics (speech analytics); (3) video analytics (Video Content Analysis - VCA); (4) predictive analytics
S2Social media big data analytics: A survey[4]Ghani et al. (2019)(1) Descriptive analytics (post-mortem analysis); (2) diagnostic analytics; (3) predictive analytics; (4) prescriptive analytics
S3Big data and social media analytics[1]Dhawan and Zanini (2014)(1) Text analytics; (2) web analytics
S4Big data and the brave new world of social media research[45]Schroeder (2014)Text analytics
S5Web-based collaborative big data analytics on big data as a service platform[46]Park et al. (2015)Video analytics
S6Social set visualizer: A set theoretical approach to big social data analytics of real-world events[47]Flesch et al. (2015)(1) Text analytics; (2) visual analytics
S7Social set analysis: A set theoretical approach to big data analytics[34]Vatrapu et al. (2016)(1) Visual analytics; (2) predictive analytics; (3) prescriptive analytics; (4) descriptive analytics; (5) text analytics
S8Social media analytics based product improvement framework[48]Su and Chen (2016)(1) Social media analytics (Social Media Product Improvement Framework (SM-PIF)); (2) text analytics (Social Media Product Improvement Framework (SM-PIF))
S9Evolving analytics for e-commerce applications: Utilizing big data and social media extensions[26]Aivalis et al. (2016)Text analytics (log file analyzer)
S10Big social data analytics of changes in consumer behaviour and opinion of a TV broadcaster[49]Hennig et al. (2016)(1) Text analytics (text classification); (2) visual analytics
S11Social media analytics based on big data[6]Shaikh et al. (2017)Text analytics (sentiment analytics)
S12The role of artificial intelligence in social media big data analytics for disaster management - initial results of a systematic literature review[7]Nunavath and Goodwin (2018)(1) Text analytics (text classification); (2) image analytics (image classification)
S13The impact of sentiment analysis on social media to assess customer satisfaction: Case of rwanda[35]Ngaboyamahina and Yi (2019)Text analytics (sentiment analysis)
S14A glimpse on big data analytics in the framework of marketing strategies[9]Ducange et al. (2018)(1) Text analytics; (2) web analytics
S15Social set analysis: Four demonstrative case studies[37]Vatrapu et al. (2015)(1) Predictive analytics; (2) descriptive analytics; (3) perspective analytics; (4) visual analytics
S16Social media analytics and internet of Things: Survey[28]Ayele and Juell-Skielse (2017)Text analytics
S17Understanding customer experience diffusion on social networking services by big data analytics[8]Piccialli and Jung (2017)Text analytics (content analysis)
S18Cyber risk prediction through social media big data analytics and statistical machine learning[33]Subroto and Apriyana (2019)(1) Text analytics; (2) predictive analytics
S19Social media, big data, and mental health: Current advances and ethical implications[50]Conway and O’Connor (2016)Text analytics
S20Social media analytics for enterprises: Typology, methods, and processes[31]Lee (2018)(1) Text analytics; (2) image analytics; (3) video analytics

10.26599/BDMA.2022.9020009.T007Techniques used in each of social media big data analytics.

BDA typeTechnique or algorithmWorking data type
BDA 1: Text analytics (text mining) / Text classification in Refs. [1,3,4,6,7,8,9,26,28,31,33,34,35,45,47,48,49,50]Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), Gibbs Sampling Approach, Latent Dirichlet Allocation Algorithm, Random Forests (RF), Decision Tree (DT), Information Extraction (Entity Recognition and Relation Extraction), Sentiment Analysis/Opinion Mining (Document Level, Sentence Level, Aspect Based, Location (Country) Based, Timestamp Based, and Followers Count Based), Lexical Resource Approach, Probabilistic Neural Network, Unstructured Data Normalizer (UDN), Text Summarization (Extractive Approach and Abstractive Approach), Social Influence Analysis, Natural Language Processing (Information Retrieval based Approach, Knowledge based Approach, and Hybrid Approach), Social Data Analytics Tool (SODATO), Support Vector Machine (SVM), Nave Bayesian classifiers (NB), Logistic Regression (LR), Multinomial Logistic Regression, Restricted Boltzmann Machine, Message Content Analysis, Non-parametric ANOVA Analysis, Cluster Analysis, Cluster Dendrogram Analysis, Histogram Analysis, Word Cloud and Commonality analysis, Pyramid Analysis, Cyber Risk Analysis, Social Network Analysis, Statistical Analysis (Markov chain Monte Carlo methods, regression models, and factor analysis), Trend Analysis, Extended Log File Analyzer (cross correlation, self-updating system, customize the configuration, Near Real Time Extensions (NRTE)), Social Media Product Improvement Framework - SM-PIF (Contextual Information Retrieval (Feature Based Ontology (FBO) and Extraction and Storage (ES)), Feature Improvement Recommendation (Product Recommendation Service (PRS)), Artificial Neural Networks (ANN), Swarm Intelligence, Evolutionary Computation, Deep Learning, Formal Model, and Fuzzy LogicStructured and unstructured
BDA 2: Image analytics (image classification)[7,31]Convolutional Neural Networks (CNN), Support Vector Machine (SVM), Linear SVM, Statistical Analysis of tag data, demographic data, download frequency, etc.Unstructured
BDA 3: Audio analytics (speech analytics)[3]Transcript-based Approach (large-vocabulary continuous speech recognition (LVCSR)) and Phonetic-based ApproachUnstructured
BDA 4: Video analytics (Video Content Analysis (VCA))[3,31,46]CCTV metadata analytic, Modified CCTV VMS (Video Management System), Server-based Approach and Edge-based Approach, and Statistical Analysis by number of users, response rate, subject, and locationUnstructured
BDA 5: Predictive analytics[3,4,33,34,37]Naive Bayes, K-nearest Neighbors, Support Vector Machines, Decision Trees, Artificial Neural Networks, Statistical Method, Modeling Machine Learning, Game Theory, Google Prediction API, Social Set Analysis, Linear Regression, Social Graph Theory (actors, actions, activities, and artifacts), and Social Text Theory (topics, keywords, pronouns, and sentiments)Structured and unstructured
BDA 6: Descriptive analytics (post-mortem Analysis)[4,34,37]Social Graph Analysis, Social Text Analysis, Social Set Analysis, and Statistical Analysis based on historical/past dataUnstructured and structured
BDA 7: Diagnostic analytics[4]Data Discovery, Drill-down, Data Mining, Data Correlations, Data Comprehension, Data Visualization, and Search and FilterUnstructured
BDA 8: Prescriptive analytics[4,34,37]Social Set Analysis, Intensive Approach, Optimization Theory, Game Theory, and Simulation and Decision techniquesUnstructured
BDA 9: Web analytics[1,9]Google Analytics, AWStats, Amung.us, WebSTAT, Radian6, and Atlas.ti and T-LABStructured and unstructured
BDA 10: Visual Analytics[34,37,47,49]Social Set Visualizer (SoSeVi), visual analytics tool Tableau, SSA approach using D3.js libraries, and Social Data Analytics Tool (SODATO)Unstructured