Skip to main content
SpringerLink
Log in

Review of social media analytics process and Big Data pipeline

  • Review Article
  • Published:
Social Network Analysis and Mining Aims and scope Submit manuscript

Abstract

Social media analytics is a research axis focused on extracting useful insights from social media data, with the aim of helping individuals and organizations take the most optimum decisions regarding several disciplines of life (business, marketing, politics, health, etc.). In this respect, social networks, microblogging, and media-sharing websites represent striking instances of online social media, as constructed under the Web 2.0 associated technologies, targeted to promote the interaction between users and these websites, while shifting the user’s position from that of a mere consumer to that of a social data producer. Hence, huge amounts of social data turn out to be issued, thus turning into critical sources of Big Data. Actually, the traditional media analytical techniques seem obsolete and inadequate to process this huge array of unstructured social media and capture the massive data range, mainly the shifting from the batch scale to the streaming one. Such a process has culminated in injecting Big Data technologies throughout the analysis process. So, the present survey is targeted to help the concerned researchers identify the challenges encountered during the analysis process along with Big Data solutions. Indeed, the aim lies in providing a clear analytical process applicable with Big Data technologies. A systematic literature review is conducted to address the challenges facing integration of Big Data technologies, while displaying some adequate solutions. Following extensive literature search, an overall global view concerning the superposition of the social media analytics and Big Data technologies has been drawn and discussed, along with a promising potential research trend.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig.11

Similar content being viewed by others

Notes

  1. https://wearesocial.com/blog/2018/01/global-digital-report-2018.

  2. https://blog.hootsuite.com/twitter-statistics/.

  3. https://www.youtube.com/intl/eng/yt/about/press/.

  4. https://newsroom.fb.com/company-info/.

  5. http://www.statisticbrain.com/facebook-statistics/.

  6. https://vibbi.com/buy-instagram-followers.

  7. https://developers.facebook.com/docs/graph-api.

  8. https://dev.twitter.com/overview/api.

  9. https://developers.google.com/youtube/v3/?hl=de.

  10. http://twitter4j.org/en/index.html.

  11. https://www.omnicoreagency.com/twitter-statistics/.

  12. https://dev.twitter.com/streaming/overview.

  13. https://developers.google.com/youtube/v3/live/getting-started.

  14. https://developers.facebook.com/docs/graph-api.

  15. https://dev.twitter.com/overview/api.

  16. https://developers.google.com/youtube/v3/getting-started.

  17. https://developers.google.com/+/web/api/rest/.

  18. https://neo4j.com/.

  19. http://www.skytree.net/.

  20. https://www.pentaho.com/.

  21. https://www.tableau.com/.

  22. https://github.com/Jaspersoft/jasperreports.

  23. https://www.talend.com/products/talend-open-studio/.

References

  • Aasman J (2006) Allegro graph: RDF triple database. Oakland Franz Incorporated, Cidade

    Google Scholar 

  • Abbasi A, Adjeroh DA, Dredze M, Paul MJ, Zahedi FM, Zhao H, Walia N et al (2014) Social media analytics for smart health. IEEE Intell Syst 29(2):60–80

    Article  Google Scholar 

  • Abramova V, Bernardino J (2013) NoSQL databases: MongoDB vs cassandra. In: Proceedings of the international C* conference on computer science and software engineering, ACM, pp 14–22

  • Achrekar H, Gandhe A, Lazarus R, Yu S-H, Liu B (2011) Predicting flu trends using twitter data. In: Computer Communications Workshops (INFOCOM WKSHPS), 2011 IEEE Conference on. IEEE, pp 702–707

  • Ackoff RL (1989) From data to wisdom. J Appl Syst Anal 16(1):3–9

    Google Scholar 

  • Agrawal D, Bernstein P, Bertino E, Davidson S, Dayal U, Franklin M, Gehrke J, Haas L, Halevy A, Han J, Jagadish HV, Labrinidis A, Madden S, Papakonstantinou Y, Patel JM, Ramakrishnan R, Ross K, Shahabi C, Suciu D, Vaithyanathan S, Widom J (2012) Challenges and opportunities with big data—a community white paper developed by leading researchers across the United States. http://cra.org/ccc/docs/init/bigdatawhitepaper.pdf

  • Agrawal R, Kadadi A, Dai X, Andres F (2015) Challenges and opportunities with big data visualization. In: Proceedings of the 7th international conference on management of computational and collective intElligence in digital EcoSystems, ACM, pp 169–173

  • Ahamed BB, Ramkumar T, Hariharan S (2014) Data integration progression in large data source using mapping affinity. In: 7th International conference on advanced software engineering and its applications (ASEA), IEEE, pp 16–21

  • Ashwin KTK, Kammarpally P, George KM (2016) Veracity of information in twitter data: a case study. In: IEEE Computer Society BigComp, pp 129–136

  • Atikoglu B, Xu Y, Frachtenberg E, Jiang S, Paleczny M (2012) Workload analysis of a large-scale key-value store. In: Harrison PG, Arlitt MF, Casale G (eds) SIGMETRICS. ACM, New York, pp 53–64

    Chapter  Google Scholar 

  • Avvenuti M, Cresci S, Marchetti A, Meletti C, Tesconi M (2014) EARS (earthquake alert and report system): a real time decision support system for earthquake crisis management. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 1749–1758

  • Avvenuti M, Cresci S, Marchetti A, Meletti C, Tesconi M (2016) Predictability or early warning: using social media in modern emergency response. IEEE Internet Comput 20(6):4–6

    Article  Google Scholar 

  • Baquero AV, Palacios RC, Molloy O (2016) Real-time business activity monitoring and analysis of process performance on big-data domains. Telematics Inform 33(3):793–807

    Article  Google Scholar 

  • Baskar S, Arockiam L, Charles S (2013) A systematic approach on data pre-processing in data mining. Compusoft 2(11):335

    Google Scholar 

  • Batrinca B, Treleaven PC (2015) Social media analytics: a survey of techniques, tools and platforms. AI Soc 30:89–116

    Article  Google Scholar 

  • Belcastro L, Marozzo F, Talia D (2018) Programming models and systems for Big Data analysis. Int J Parallel Emerg Distrib Syst. https://doi.org/10.1080/17445760.2017.1422501

  • Bermbach D, Müller S, Eberhardt J, Tai S (2015) Informed schema design for column store-based database services. In: SOCA, IEEE Computer Society, pp 163–172

  • Bhuta S, Doshi A, Doshi U, Narvekar M (2014) A review of techniques for sentiment analysis Of Twitter data. In: International conference on issues and challenges in intelligent computing techniques (ICICT), IEEE, pp. 583–591

  • Bocconi S, Bozzon A, Psyllidis A, Bolivar CT, Houben G-J (2015) Social glass: a platform for urban analytics and decision-making through heterogeneous social data. In: Gangemi A, Leonardi S, Panconesi A (eds) WWW (companion volume). ACM, New York, pp 175–178

    Chapter  Google Scholar 

  • Bohlouli M, Dalter J, Dornhöfer M, Zenkert J, Fathi M (2015) Knowledge discovery from social media using big data-provided sentiment analysis (SoMABiT). J Inf Sci 41(6):779–798

    Article  Google Scholar 

  • Bothos E, Apostolou D, Mentzas G (2010) Using social media to predict future events with agent-based markets. IEEE Intell Syst 25(6):50–58

    Article  Google Scholar 

  • Cambria E, Wang H, White B (2014) Guest editorial: big social data analysis. Knowl-Based Syst 69:1–2

    Article  Google Scholar 

  • Cao J, Chawla S, Wang Y, Wu H (2017) Programming platforms for Big Data analysis. In: Handbook of big data technologies. Springer, pp 65–99

  • Carlson JL (2013) Redis in action. Manning Publications Co., Shelter Island

    Google Scholar 

  • Chang F, Dean J, Ghemawat S, Hsieh WC, Wallach DA, Burrows M, Chandra T et al (2008) Bigtable: a distributed storage system for structured data. ACM Trans Comput Syst (TOCS) 26(2):4

    Article  Google Scholar 

  • Chang RM, Kauffman RJ, Kwon Y (2014) Understanding the paradigm shift to computational social science in the presence of big data. Decis Support Syst 63:67–80

    Article  Google Scholar 

  • Chen CP, Zhang C-Y (2014) Data-intensive applications, challenges, techniques and technologies: a survey on Big Data. Inf Sci 275:314–347

    Article  Google Scholar 

  • Chen M, Ebert D, Hagen H, Laramee RS, Van Liere R, Ma K-L, Ribarsky W et al (2009) Data, information, and knowledge in visualization. IEEE Comput Gr Appl 29(1):1–10

    Article  Google Scholar 

  • Cheng X, Liu J, Dale C (2013) Understanding the characteristics of internet short video sharing: a YouTube-based measurement study. IEEE Trans Multimed 15(5):1184–1194

    Article  Google Scholar 

  • Ching A, Edunov S, Kabiljo M, Logothetis D, Muthukrishnan S (2015) One Trillion edges: graph processing at Facebook-scale. PVLDB 8:1804–1815

    Google Scholar 

  • Chintapalli S, Dagit D, Evans B, Farivar R, Graves T, Holderbaugh M, Liu Z, Nusbaum K, Patil K, Peng B, Poulosky P (2016) Benchmarking streaming computation engines: storm, flink and spark streaming. In: IPDPS workshops, IEEE Computer Society, pp 1789–1792

  • Chodorow K (2013) MongoDB: the definitive guide. O”Reilly Media, Inc., Newton

    Google Scholar 

  • Corbellini A, Mateos C, Zunino A, Godoy D, Schiaffino S (2017) Persisting big-data: the NoSQL landscape. Inf Syst 63:1–23

    Article  Google Scholar 

  • Cormode G, Krishnamurthy B (2008) Key differences between Web 1.0 and Web 2.0. First Monday 13(6)

  • Dang Y, Zhang Y, Hu PJ-H, Brown SA, Ku Y, Wang J-H, Chen H (2014) An integrated framework for analyzing multilingual content in Web 2.0 social media. Decis Support Syst 61:126–135

    Article  Google Scholar 

  • Dean J, Ghemawat S (2008) MapReduce: simplified data processing on large clusters. Commun ACM 51(1):107–113

    Article  Google Scholar 

  • Dean J, Ghemawat S (2010) MapReduce: a flexible data processing tool. Commun ACM 53:72–77

    Article  Google Scholar 

  • Dredze M (2012) How social media will change public health. IEEE Intell Syst 27(4):81–84

    Article  Google Scholar 

  • Elgendy N, Elragal A (2014) Big data analytics: a literature review paper. In Perner P (eds) Advances in data mining. Applications and theoretical aspects. ICDM. Lecture notes in computer science, vol 8557. Springer, Cham

  • Esposito C, Ficco M, Palmieri F, Castiglione A (2015) A knowledge-based platform for Big Data analytics based on publish/subscribe services and stream processing. Knowl-Based Syst 79:3–17

    Article  Google Scholar 

  • Fan W, Bifet A (2013) Mining big data: current status, and forecast to the future. ACM SIGKDD Explor Newsl 14(2):1–5

    Article  Google Scholar 

  • Furht B, Villanustre F (2016) Introduction to Big Data. Big Data technologies and applications. Springer, Berlin, pp 3–11

    Book  Google Scholar 

  • Gandomi A, Haider M (2015) Beyond the hype: big data concepts, methods, and analytics. Int J Inf Manag 35(2):137–144

    Article  Google Scholar 

  • Auradkar A, Botev C, Das S, De Maagd D, Feinberg A, Ganti P, Gao L, et al. (2012) Data infrastructure at linkedin. In: IEEE 28th international conference on data engineering (ICDE), IEEE, pp 1370–1381

  • Ghemawat S, Gobioff H, Leung S-T (2003) The Google file system. ACM SIGOPS operating systems review, vol 37. ACM, New York, pp 29–43

    Google Scholar 

  • Han J, Kamber M, Pei J (2011a) Data mining: concepts and techniques. Elsevier, Amsterdam

    MATH  Google Scholar 

  • Han J, Haihong E, Le G, Du J (2011b) Survey on NoSQL database. In: 6th international conference on pervasive computing and applications (ICPCA), IEEE, pp 363–366

  • Haryadi AF, Hulstijn J, Wahyudi A, Voort H, van der, Janssen M (2016) Antecedents of big data quality: an empirical examination in financial service organizations. In: IEEE international conference on Big Data (Big Data), IEEE, pp 116–121

  • Hashem IAT, Yaqoob I, Anuar NB, Mokhtar S, Gani A, Khan SU (2015) The rise of “big data” on cloud computing: review and open research issues. Inf Syst 47:98–115

    Article  Google Scholar 

  • He W, Wang F-K, Akula V (2017) Managing extracted knowledge from big social media data for business decision making. J Knowl Manag 21(2):275–294

    Article  Google Scholar 

  • Hiba S, Mohamed Ali HT, Mohamed BA (2018) Popularity metrics’ normalization for social media entities. In: 20th International Conference on Enterprise Information Systems, pp 525–535

  • Hu H, Wen Y, Chua TS, Li X (2014) Toward scalable systems for big data analytics: a technology tutorial. IEEE Access 2:652–687

    Article  Google Scholar 

  • Imran M, Castillo C, Diaz F, Vieweg S (2015) Processing social media messages in mass emergency: a survey. ACM Comput Surv 47(4):67

    Article  Google Scholar 

  • Isard M, Budiu M, Yu Y, Birrell A, Fetterly D (2007) Dryad: distributed data-parallel programs from sequential building blocks. ACM SIGOPS operating systems review, ACM, vol 41, pp 59–72

  • Jagadish H, Gehrke J, Labrinidis A, Papakonstantinou Y, Patel JM, Ramakrishnan R, Shahabi C (2014) Big data and its technical challenges. Commun ACM 57(7):86–94

    Article  Google Scholar 

  • Ji X, Chun SA, Cappellari P, Geller J (2017) Linking and using social media data for enhancing public health analytics. J Inf Sci 43(2):221–245

    Article  Google Scholar 

  • Jure L (2011) Social media analytics: tracking, modeling and predicting the flow of information through networks. In: Proceedings of the 20th international conference companion on World wide web (WWW ‘11). ACM, New York, NY, USA, pp 277–278

  • Kaisler SH, Armour F, Espinosa JA, Money WH (2013) Big Data: issues and challenges moving forward. In: IEEE Computer Society HICSS, pp 995–1004

  • Kanhabua N, Romano S, Stewart A, Nejdl W (2012a) Supporting temporal analytics for health-related events in microblogs. In: Proceedings of the 21st ACM international conference on Information and knowledge management, CIKM’12, ACM, Maui, Hawaii, pp 2686–2688

  • Kaplan AM, Haenlein M (2010) Users of the world, unite! The challenges and opportunities of Social Media. Bus Horiz 53(1):59–68

    Article  Google Scholar 

  • Karpenko A, Aarabi P (2011) Tiny videos: a large data set for nonparametric video retrieval and frame classification. IEEE Trans Pattern Anal Mach Intell 33(3):618–630

    Article  Google Scholar 

  • Khan N, Yaqoob I, Hashem IAT, Inayat Z, Mahmoud Ali WK, Alam M, Shiraz M et al (2014) Big data: survey, technologies, opportunities, and challenges. Sci World J 2014:1–18

    Google Scholar 

  • Kotsilieris T, Pavlaki A, Christopoulou SC, Anagnostopoulos I (2017) The impact of social networks on health care. Social Netw Anal Min 7(1):18:1–18:6

  • Kumar V, Chadha A (2012) Mining association rules in student’s assessment data. Int J Comput Sci Issues 9(5):211–216

    Google Scholar 

  • Lennon, J. (2009). Introduction to couchdb. Beginning CouchDB, pp 3–9

  • Li N, Wu DD (2010) Using text mining and sentiment analysis for online forums hotspot detection and forecast. Decis Support Syst 48(2):354–368

    Article  Google Scholar 

  • Low Y, Bickson D, Gonzalez J, Guestrin C, Kyrola A, Hellerstein JM (2012) Distributed GraphLab: a framework for machine learning and data mining in the cloud. Proc VLDB Endow 5(8):716–727

    Article  Google Scholar 

  • Magnusson J (2012) Social network analysis utilizing Big Data Technology. https://www.diva-portal.org/smash/get/diva2:509757/FULLTEXT01.pdf

  • Malewicz G, Austern MH, Bik AJ, Dehnert JC, Horn I, Leiser N, Czajkowski G (2010) Pregel: a system for large-scale graph processing. In: Proceedings of the ACM SIGMOD international conference on management of data, ACM, pp 135–146

  • Manyika J, Chui M, Brown B, Bughin J, Dobbs R, Roxburgh C, Byers A (2011) Big Data: the next frontier for innovation, competition, and productivity

  • Mendoza M, Poblete B, Castillo C (2010) Twitter under crisis: can we trust what we RT? In: Giles CL, Mitra P, Perisic I, Yen J, Zhang H (eds) SOMA@KDD. ACM, New York, pp 71–79

    Google Scholar 

  • Meng X, Bradley J, Yavuz B, Sparks E, Venkataraman S, Liu D, Freeman J et al (2016) Mllib: machine learning in apache spark. J Mach Learn Res 17(34):1–7

    MathSciNet  MATH  Google Scholar 

  • Middleton SE, Middleton L, Modafferi S (2014) Real-time crisis mapping of natural disasters using social media. IEEE Intell Syst 29(2):9–17

    Article  Google Scholar 

  • Mikolov T, Deoras A, Povey D, Burget L, Cernock J (2011) Strategies for training large scale neural network language models. In: IEEE Workshop on automatic speech recognition and understanding (ASRU), IEEE, pp 196–201

  • Neumeyer L, Robbins B, Nair A, Kesari A (2010) S4: distributed stream computing platform. In: IEEE international conference on data mining workshops (ICDMW), IEEE, pp 170–177

  • Newman R, Chang V, Walters RJ, Wills GB (2016) Web 2.0–the past and the future. Int J Inf Manag 36(4):591–598

    Article  Google Scholar 

  • Nguyen DT, Hwang D, Jung JJ (2014) Time-frequency social data analytics for understanding social big data. In: IDC, Studies in Computational Intelligence, vol 570. Springer, pp 223–228

  • Oh C, Sasser S, Almahmoud S (2015) Social media analytics framework: the case of Twitter and Super Bowl ads. J Inf Technol Manag 26(1):1–18

    Google Scholar 

  • Olshannikova E, Ometov A, Koucheryavy Y, Olsson T (2016) Visualizing Big Data. In: Big Data technologies and applications, Springer, pp 101–131

  • Orgaz GB, Jung JJ, Camacho D (2016) Social big data: recent achievements and new challenges. Inf Fus 28:45–59

    Article  Google Scholar 

  • Oussous A, Benjelloun F-Z, Lahcen AA, Belfkih S (2017) Big Data technologies: a survey. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2017.06.001

  • Owen S, Owen S (2012) Mahout in action. Manning Publications Co., Shelter Island

    Google Scholar 

  • Peng S, Wang G, Xie D (2017) Social influence analysis in social networking big data: opportunities and challenges. IEEE Netw 31(1):11–17

    Article  Google Scholar 

  • Radicati S, Hoang Q (2011) Email statistics report 2011–2015. The Radicati Group, Inc. A Technology Market Research Firm

  • Rahmani A, Chen AC-L, Sarhan A, Jida J, Rifaie M, Alhajj R (2014) Social media analysis and summarization for opinion mining: a business case study. Social Netw Anal Min 4(1):171

    Article  Google Scholar 

  • Reuter C, Scholl S (2014) Technical limitations for designing applications for social media. In: Butz A, Koch M, Schlichter JH (eds) Mensch & Computer workshop band. De Gruyter Oldenbourg, Berlin, pp 131–139

    Google Scholar 

  • Rowley J (2007) The wisdom hierarchy: representations of the DIKW hierarchy. J Inf Sci 33(2):163–180

    Article  Google Scholar 

  • Sakaki T, Okazaki M, Matsuo Y (2013) Tweet analysis for real-time event detection and earthquake reporting system development. IEEE Trans Knowl Data Eng 25(4):919–931

    Article  Google Scholar 

  • Sakr S (2016) Large-scale graph processing systems. In: Big Data 2.0 Processing Systems: A Survey, Springer, Cham, pp 53–73

  • Santhanam T, Padmavathi M (2014) Comparison of K-means clustering and statistical outliers in reducing medical datasets. In: International conference on science engineering and management research (ICSEMR), IEEE, pp 1–6

  • Sapountzi A, Psannis KE (2016) Social networking data analysis tools & challenges. Future Gener Comput Sys. https://doi.org/10.1016/j.future.2016.10.019

  • Schroeck M, Shockley R, Smart J, Romero-Morales D, Tufano P (2012) Analytics: the real-world use of big data: How innovative enterprises extract value from uncertain data, Executive Report. In: IBM Institute for Business Value and Said Business School at the University of Oxford

  • Selvan LGS, Moh T-S (2015) A framework for fast-feedback opinion mining on Twitter data streams. In: CTS, IEEE, pp 314–318

  • Siddiqa A, Hashem IAT, Yaqoob I, Marjani M, Shamshirband S, Gani A, Nasaruddin F (2016) A survey of big data management: taxonomy and state-of-the-art. J Netw Comput Appl 71:151–166

    Article  Google Scholar 

  • Siddiqa A, Karim A, Gani A (2017) Big data storage technologies: a survey. Front IT & EE 18:1040–1070

    Google Scholar 

  • Skoric MM, Poor ND, Achananuparp P, Lim E-P, Jiang J (2012) Tweets and votes: a study of the 2011 Singapore General Election. In: IEEE Computer Society, HICSS, pp 2583–2591

  • Stenmark D (2002) Information vs. knowledge: the role of intranets in knowledge management. In: Proceedings of HICSS. IEEE Press

  • Stieglitz S, Dang-Xuan L (2013) Social media and political communication: a social media analytics framework. Soc Netw Anal Min 3(4):1277–1291

    Article  Google Scholar 

  • Stieglitz S, Dang-Xuan L, Bruns A, Neuberger C (2014) Social media analytics. Wirtschaftsinformatik 56(2):101–109

    Article  Google Scholar 

  • Stieglitz S, Mirbabaie M, Ross B, Neuberger C (2018) Social media analytics—challenges in topic discovery, data collection, and data preparation. Int J Inf Manag 39:156–168

    Article  Google Scholar 

  • Storey VC, Song I-Y (2017) Big data technologies and management: what conceptual modeling can do. Data Knowl Eng 108:50–67

    Article  Google Scholar 

  • Strohbach M, Daubert J, Ravkin H, Lischka M (2016) Big data storage. In: New horizons for a data-driven economy, Springer, Cham, pp 119–141

  • Taylor RC (2010) An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics. BMC Bioinf 11(12):S1

    Article  MathSciNet  Google Scholar 

  • Uddin MF, Gupta N et al. (2014) Seven V’s of Big Data understanding Big Data to extract value. In: American Society for Engineering Education (ASEE Zone 1), Zone 1 Conference of the IEEE, pp 1–5

  • Vatrapu R, Mukkamala RR, Hussain A, Flesch B (2016) Social set analysis: a set theoretical approach to big data analytics. IEEE Access 4:2542–2571

    Article  Google Scholar 

  • Vickery G, Wunsch-Vincent S (2007) Participative web and user-created content: Web 2.0 wikis and social networking. Organization for Economic Cooperation and Development (OECD)

  • Wang WY, Pauleen DJ, Zhang T (2016) How social media applications affect B2B communication and improve business performance in SMEs. Ind Mark Manag 54:4–14

    Article  Google Scholar 

  • Wang H, Xu Z, Pedrycz W (2017) An overview on the roles of fuzzy set techniques in big data processing: trends, challenges and opportunities. Knowl-Based Syst 118:15–30

    Article  Google Scholar 

  • White T (2012) Hadoop: the definitive guide. O”Reilly Media, Newton

    Google Scholar 

  • Win SSM, Aung TN (2017) Target oriented tweets monitoring system during natural disasters. In: Uehara K, Nakamura M (eds) ICIS, IEEE Computer Society, pp 143–148

  • Wu Y, Cao N, Gotz D, Tan Y-P, Keim DA (2016) A survey on visual analytics of social media data. IEEE Trans Multimed 18:2135–2148

    Article  Google Scholar 

  • Wu D, Sakr S, Zhu L (2017) Big data storage and data models. In: Handbook of big data technologies, Springer, Cham, pp 3–29

  • Xin R, Rosen J, Zaharia M, Franklin MJ, Shenker S, Stoica I (2012) Shark: SQL and rich analytics at scale. CoRR. abs/1211.6176

  • Yaqoob I, Hashem IAT, Gani A, Mokhtar S, Ahmed E, Anuar NB, Vasilakos AV (2016) Big data: from beginning to future. Int J Inf Manag 6(6):1231–1247

    Article  Google Scholar 

  • Yaqub U, Chun SA, Atluri V, Vaidya J (2017) Sentiment based analysis of tweets during the US Presidential Elections. In: Hinnant CC, Ojo A (eds) DG.O, ACM, New York, pp 1–10

  • Zeng D, Chen H, Lusch R, Li S-H (2010) Social media analytics and intelligence. IEEE Intell Syst 25(6):13–16

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Multimedia, InfoRmation systems and Advanced Computing Laboratory, Sfax University, Sfax, Tunisia

    Hiba Sebei, Mohamed Ali Hadj Taieb & Mohamed Ben Aouicha

Authors
  1. Hiba Sebei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamed Ali Hadj Taieb
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohamed Ben Aouicha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hiba Sebei.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sebei, H., Hadj Taieb, M.A. & Ben Aouicha, M. Review of social media analytics process and Big Data pipeline. Soc. Netw. Anal. Min. 8, 30 (2018). https://doi.org/10.1007/s13278-018-0507-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13278-018-0507-0

Keywords

Search

Navigation