AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (4.4 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
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
Show Author Information

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.

References

[1]
V. Dhawan and N. Zanini, Big data and social media analytics, Res. Matters A Cambridge Assess. Publ., no. 18, pp. 36-41, 2014.
[2]
K. Smith, 126 amazing social media statistics and facts, https://www.brandwatch.com/blog/amazing-social-media-statistics-and-facts/, 2019.
[3]
A. Gandomi and M. Haider, Beyond the hype: Big data concepts, methods, and analytics, Int. J. Inf. Manage., vol. 35, no. 2, pp. 137-144, 2015.
[4]
N. A. Ghani, S. Hamid, I. A. Targio Hashem, and E. Ahmed, Social media big data analytics: A survey, Comput. Human Behav., vol. 101, pp. 417-428, 2019.
[5]
P. V. Paul, K. Monica, and M. Trishanka, A survey on big data analytics using social media data, in Proc. 2017 Innov. Power Adv. Comput. Technol. (i-PACT), Vellore, India, 2017, pp. 1-4.
[6]
F. Shaikh, F. Rangrez, A. Khan, and U. Shaikh, Social media analytics based on big data, in Proc. 2017 Int. Conf. Intell. Comput. Control. (I2C2), Coimbatore, India, 2017, pp. 1-6.
[7]
V. Nunavath and M. Goodwin, The role of artificial intelligence in social media big data analytics for disaster management-initial results of a systematic literature review, in Proc. 2018 5th Int. Conf. Inf. Commun. Technol. Disaster Manag. (ICT-DM), Sendai, Japan, 2018, pp. 1-4.
[8]
F. Piccialli and J. E. Jung, Understanding customer experience diffusion on social networking services by big data analytics, Mob. Networks Appl., vol. 22, no. 4, pp. 605-612, 2017.
[9]
P. Ducange, R. Pecori, and P. Mezzina, A glimpse on big data analytics in the framework of marketing strategies, Soft Comput., vol. 22, no. 1, pp. 325-342, 2018.
[10]
P. Grover and A. K. Kar, Big data analytics: A review on theoretical contributions and tools used in literature, Glob. J. Flex. Syst. Manag., vol. 18, no. 3, pp. 203-229, 2017.
[11]
J. Amudhavel, V. Padmapriya, V. Gowri, K. Lakshmipriya, K. P. Kumar, and B. Thiyagarajan, Perspectives, motivations, and implications of big data analytics, in Proc. 2015 International Conference on Advanced Research in Computer Science Engineering & Technology (ICARCSET), Unnao, India, 2015, pp. 1-5.
[12]
M. Gupta and J. F. George, Toward the development of a big data analytics capability, Inf. Manag., vol. 53, no. 8, pp. 1049-1064, 2016.
[13]
L. Cao, Data science: Challenges and directions, Communication of the ACM, vol. 60, no. 8, pp. 59-68, 2017.
[14]
Z. Sun, K. Strang, and R. Li, Big data with ten big characteristics, .
[15]
B. Sena, A. P. Allian, and E. Y. Nakagawa, Characterizing big data software architectures: A systematic mapping study, in Proc. 11th Brazilian Symposium on Software Components, Architectures, and Reuse, New York, NY, USA, 2017, pp. 1-10.
[16]
D. Laney, 3D data management: Controlling data volume, velocity and variety, META Group Research Note, https://studylib.net/doc/8647594/3d-data-management-controlling-data-volume-velocity-an…, 2001.
[17]
Gartner, Douglas Laney, https://www.gartner.com/en/experts/douglas-laney, 2022.
[18]
Gartner, Mark A. Beyer, https://www.gartner.com/en/experts/mark-beyer, 2022.
[19]
M. S. Rahman and H. Reza, Systematic mapping study of non-functional requirements in big data system, in Proc. 2020 IEEE International Conference on Electro Information Technology (EIT), Chicago, IL, USA, 2020, pp. 25-31.
[20]
M. S. Rahman and H. Reza, Big data analytics in social media: A triple T (types, techniques, and taxonomy) study, in Proc. ITNG 2021 18th International Conference on Information Technology-New Generations, Las Vegas, NV, USA, 2021, pp 479-487.
[21]
M. A. Beyer and D. Laney, The importance of “big data”: A definition, https://www.gartner.com/doc/2057415, 2012.
[22]
What is big data?-A definition with five Vs, https://blog.unbelievable-machine.com/en/what-is-big-data-definition-five-vs, 2018.
[23]
N. Dave, 4 major ways in which big data is impacting social media marketing, https://insidebigdata.com/2018/10/06/4-major-ways-big-data-impacting-social-media-marketing/, 2018.
[24]
[25]
W. Contributors, Timeline of social media, https://en.wikipedia.org/wiki/Timeline_of_social_media, 2019.
[26]
C. J. Aivalis, K. Gatziolis, and A. C. Boucouvalas, Evolving analytics for e-commerce applications: Utilizing big data and social media extensions, in Proc. 2016 Int. Conf. Telecommun. Multimedia (TEMU), Heraklion, Greece, 2016, pp. 1-6.
[27]
D. Chaffey, What happens online in 60 seconds in 2021? https://www.smartinsights.com/internet-marketing-statistics/happens-online-60-seconds/, 2021.
[28]
W. Y. Ayele and G. Juell-Skielse, Social media analytics and internet of things: Survey, in Proc. 1st International Conference on Internet of Things and Machine Learning, Liverpool, UK, 2017, pp. 1-11.
[29]
J. Spencer, 101 social networking sites you need to know about in 2022, https://makeawebsitehub.com/social-media-sites/, 2021.
[30]
W. Contributors, List of social bookmarking websites, https://en.wikipedia.org/wiki/List_of_social_bookmarking_websites, 2019.
[31]
I. Lee, Social media analytics for enterprises: Typology, methods, and processes, Bus. Horiz., vol. 61, no. 2, pp. 199-210, 2018.
[32]
L. Paddington, The best social networks for moms, https://www.ranker.com/list/mom-social-networks/liz-paddington, 2019.
[33]
A. Subroto and A. Apriyana, Cyber risk prediction through social media big data analytics and statistical machine learning, J. Big Data, vol. 6, no. 1, p. 50, 2019.
[34]
R. Vatrapu, R. R. Mukkamala, A. Hussain, and B. Flesch, Social set analysis: A set theoretical approach to big data analytics, IEEE Access, vol. 4, pp. 2542-2571, 2016.
[35]
M. Ngaboyamahina and S. Yi, The impact of sentiment analysis on social media to assess customer satisfaction: Case of Rwanda, in Proc. 2019 IEEE 4th Int. Conf. Big Data Anal., Suzhou, China, 2019, pp. 356-359.
[36]
Wikipedia contributors, ResearchGate, In Wikipedia, the free encyclopedia, https://en.wikipedia.org/w/index.php?title=ResearchGate&oldid=1042963415, 2021.
[37]
R. Vatrapu, A. Hussain, N. B. Lassen, R. R. Mukkamala, B. Flesch, and R. Madsen, Social set analysis: Four demonstrative case studies, in Proc. 2015 International Conference on Social Media & Society, Toronto, Canada, 2015, pp. 1-9.
[38]
A. Katal, M. Wazid, and R. H. Goudar, Big data: Issues, challenges, tools, and good practices, in Proc. 2013 6th Int. Conf. Contemp. Comput. (IC3), Noida, India, 2013, pp. 404-409.
[39]
P. Di Francesco, I. Malavolta, and P. Lago, Research on architecting microservices: Trends, focus, and potential for industrial adoption, in Proc. 2017 IEEE International Conference on Software Architecture (ICSA), Gothenburg, Sweden, 2017, pp. 21-30.
[40]
B. Kitchenham and S. M. Charters, Guidelines for performing systematic literature reviews in software engineering, Tech. Rep. EBSE-2007-01, Keele University and Durham University, Keele and Durham, UK, 2007.
[41]
K. Petersen, S. Vakkalanka, and L. Kuzniarz, Guidelines for conducting systematic mapping studies in software engineering: An update, Information & Software Technology, vol. 64, pp 1-18, 2015.
[42]
ACM digital library, https://dl.acm.org/, 2022.
[44]
ScienceDirect, https://www.sciencedirect.com/, 2022.
[45]
R. Schroeder, Big data and the brave new world of social media research, Big Data Soc., vol. 1, no. 2, pp. 1-11, 2014.
[46]
K. Park, M. C. Nguyen, and H. Won, Web-based collaborative big data analytics on big data as a service platform, in Proc. 2015 7th Int. Conf. Adv. Commun. Technol. (ICACT), PyeongChang, Republic of Korea, 2015, pp. 564-567.
[47]
B. Flesch, R. Vatrapu, R. R. Mukkamala, and A. Hussain, Social set visualizer: A set theoretical approach to big social data analytics of real-world events, in Proc. 2015 IEEE Int. Conf. Big Data, Santa Clara, CA, USA, 2015, pp. 2418-2427, 2015.
[48]
C. J. Su and Y. A. Chen, Social media analytics based product improvement framework, in Proc. 2016 IEEE Int. Symp. Comput. Consum. Control. (IS3C), Xi’an, China, 2016, pp. 393-396.
[49]
A. Hennig, A. -S. Åmodt, H. Hernes, H. M. Nygårdsmoen, P. A. Larsen, R. R. Mukkamala, B. Flesch, A. Hussain, and R. K. Vatrapu, Big social data analytics of changes in consumer behaviour and opinion of a TV broadcaster, in Proc. 2016 IEEE Int. Conf. Big Data, Washington, DC, USA, 2016, pp. 3839-3848.
[50]
M. Conway and D. O’Connor, Social media, big data, and mental health: Current advances and ethical implications, Curr. Opin. Psychol., vol. 9, pp. 77-82, 2016.
[51]
M. Thangaraj and S. Amutha, Similarity between sentiment analysis and social network analysis, Int. J. Sci. Eng. Res., vol. 8, no. 4, pp. 1526-1532, 2017.
[52]
R. Toujani and J. Akaichi, Fuzzy sentiment classification in social network Facebook’ statuses mining, in Proc. 2016 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), Hammamet, Tunisia, 2016, pp. 393-397.
[53]
J. Barnes, L. Øvrelid, and E. Velldal, Sentiment analysis is not solved! Assessing and probing sentiment classification, arXiv preprint arXiv: 1906.05887, 2019.
[54]
R. Obiedat, R. Qaddoura, A. M. Al-Zoubi, L. Al-Qaisi, O. Harfoushi, M. Alrefai, and H. Faris, Sentiment analysis of customers’ reviews using a hybrid evolutionary SVM-based approach in an imbalanced data distribution, IEEE Access, .
[55]
Wikipedia contributors, Google analytics, Wikipedia, The free encyclopedia, https://en.wikipedia.org/w/index.php?title=Google_Analytics&oldid=1071228134, 2022.
Big Data Mining and Analytics
Pages 228-244
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

4299

Views

1625

Downloads

27

Crossref

12

Web of Science

24

Scopus

0

CSCD

Altmetrics

Received: 09 December 2021
Revised: 18 February 2022
Accepted: 01 April 2022
Published: 09 June 2022
© The author(s) 2022.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

Return