Skip to main content
SpringerLink
Log in

Multimedia Social Big Data: Mining

  • Chapter
  • First Online:
Multimedia Big Data Computing for IoT Applications

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 163))

Abstract

The rapid evolution and adoption of the SMAC (Social media, Mobile, Analytics and Cloud) technology paradigm, has generated massive volumes of human-centric, real-time, multimodal, heterogeneous data. Human-sourced information from social networks, process-mediated data from business systems and machine-generated data from Internet-of-Things are the three primary sources of big data which define the richness and scale of multimedia content available. With the proliferation of social networks (Twitter, Tumblr, Google+, Facebook, Instagram, Snapchat, YouTube, etc.), the user can post and share all kinds of multimedia content (text, image, audio, video) in the social setting using the Internet without much knowledge about the Web’s client-server architecture and network topology. This proffer novel opportunities and challenges to leverage high-diversity multimedia data in concurrence to the huge amount of social data. In recent years, multimedia analytics as a technology-based solution has attracted a lot of attention by both researchers and practitioners. The mining opportunities to analyze, model and discover knowledge from the social web applications/services are not restricted to the text-based big data, but extend to the partially unknown complex structures of image, audio and video. Interestingly, the big data is estimated to be 90% unstructured further, making it crucial to tap and analyze information using contemporary tools. The work presented is an extensive and organized overview of the multimedia social big data mining and applications. A comprehensive coverage of the taxonomy, types and techniques of Multimedia Social Big Data mining is put forward. A SWOT Analysis is done to understand the feasibility and scope of social multimedia content and big data analytics is also illustrated. Recent applications and suitable directions for future research have been identified which validate and endorse this correlation of multimedia to big data for mining social data.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 119.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. J. Oliverio, A survey of social media, big data, data mining, and analytics. J. Ind. Integr. Manag. 1850003 (2018)

    Article  Google Scholar 

  2. D. Borth, T. Chen, R. Ji, S.-F. Chang, SentiBank: large-scale ontology and classifiers for detecting sentiment and emotions in visual content, in Proceedings of the 21st ACM international conference on Multimedia, 21–25 October 2013 (Barcelona, Spain, 2013), https://doi.org/10.1145/2502081.2502268

  3. A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks, in Proceedings of the 25th International Conference on Neural Information Processing Systems, 03–06 December, 2012 (Lake Tahoe, Nevada, 2012), pp. 1097–1105

    Google Scholar 

  4. J. Weston, S. Bengio, N. Usunier, Wsabie: scaling up to large vocabulary image annotation, in Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, 16–22 July 2011 (Barcelona, Catalonia, Spain, 2011), pp. 2764–2770, https://doi.org/10.5591/978-1-57735-516-8/ijcai11-460

  5. M. Wang, D. Cao, L. Li, S. Li, R. Ji, Microblog sentiment analysis based on cross-media bag-of-words model, in Proceedings of International Conference on Internet Multimedia Computing and Service, 10–12 July 2014 (Xiamen, China, 2014), https://doi.org/10.1145/2632856.2632912

  6. A.B. Alencar, M.C.F. de Oliveira, F.V. Paulovich, Seeing beyond reading: a survey on visual text analytics. Wiley Interdiscip. Rev. Data Min. Knowl. Discov.2(6), 476–492 (2012)

    Google Scholar 

  7. I.E. Fisher, et al., The role of text analytics and information retrieval in the accounting domain. J. Emerg. Technol. Account. 7(1), 1–24 (2010)

    Article  Google Scholar 

  8. X. Hu, H. Liu, Text analytics in social media, in Mining Text Data, (Springer, Boston, MA, 2012), pp. 385–414

    Chapter  Google Scholar 

  9. C.C. Aggarwal, H. Wang, Text mining in social networks, in Social Network Data Analytics (Springer, Boston, MA, 2011), pp. 353–378

    Chapter  MATH  Google Scholar 

  10. Tobias Schreck, Daniel Keim, Visual analysis of social media data. Computer 46(5), 68–75 (2013)

    Article  Google Scholar 

  11. K. O’Halloran, A. Chua, A. Podlasov, The role of images in social media analytics: a multimodal digital humanities approach, in Visual Communication (De Gruyter, 2014), pp. 565–588

    Google Scholar 

  12. N. Diakopoulos, M. Naaman, F. Kivran-Swaine, Diamonds in the rough: social media visual analytics for journalistic inquiry. in 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) (IEEE, 2010)

    Google Scholar 

  13. Bogdan Batrinca, Philip C. Treleaven, Social media analytics: a survey of techniques, tools and platforms. AI Soc. 30(1), 89–116 (2015)

    Article  Google Scholar 

  14. Tobias Schreck, Daniel Keim, Visual analysis of social media data. Computer 46(5), 68–75 (2013)

    Article  Google Scholar 

  15. W. Mason, J.W. Vaughan, H. Wallach, Mach. Learn. 95, 257 (2014). https://doi.org/10.1007/s10994-013-5426-8

    Article  MathSciNet  Google Scholar 

  16. X. Wang, J. Yang, X. Teng et al., Feature selection based on rough sets and particle swarm optimization. Pattern Recogn. Lett. 28(4), 459–471 (2007)

    Article  Google Scholar 

  17. M.I. Jordan, T.M. Mitchell, Machine learning: trends, perspectives, and prospects. Science 349(6245), 255–260 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  18. Mohammad Ahmadi, Parthasarati Dileepan, K. Wheatley Kathleen, A SWOT analysis of big data. J. Educ. Bus. 91, 1–6 (2016). https://doi.org/10.1080/08832323.2016.1181045

    Article  Google Scholar 

  19. R. Talib, M.K. Hanif, S. Ayesha, F. Fatima, Text mining: techniques, applications and issues. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 7(11) (2016)

    Google Scholar 

  20. P. Vashisht, V. Gupta, (2015). Big data analytics techniques: a survey, pp. 264–269. https://doi.org/10.1109/icgciot.2015.7380470

  21. R. Reka Dr, K. Saraswathi, K. Sujatha Dr, A review on big data analytics. Asian J. Appl. Sci. Technol. (AJAST) 1(1), 233–234 (2017)

    Google Scholar 

  22. Carlos Castillo, Marcelo Mendoza, Barbara Poblete, Predicting information credibility in time-sensitive social media. Internet Res. 23(5), 560–588 (2013)

    Article  Google Scholar 

  23. A. Kumar, S.R. Sangwan, Rumour detection using machine learning techniques on social media, in International Conference on Innovative Computing and Communication. Lecture Notes in Networks and Systems (Springer, 2018)

    Google Scholar 

  24. A. Zubiaga, M. Liakata, R. Procter, G.W.S. Hoi, P. Tolmie, Analysing how people orient to and spread rumours in social media by looking at conversational threads. PLoS One 11(3), 1–29 (2016)

    Article  Google Scholar 

  25. M.E. Jaeger, S. Anthony, R.L. Rosnow, Who hears what from whom and with what effect a study of rumor. Personal. Soc. Psychol. Bull. 6(3), 473–478 (1980)

    Article  Google Scholar 

  26. A. Zubiaga, et al., Detection and resolution of rumours in social media: a survey. ACM Comput. Surv. (CSUR) 51(2), 32 (2018)

    Article  Google Scholar 

  27. Z. Zhao, P. Resnick, Q. Mei, Enquiring minds: early detection of rumors in social media from enquiry posts, in Proceedings of the 24th International Conference on World Wide Web (International World Wide Web Conferences Steering Committee, 2015)

    Google Scholar 

  28. A. Zubiaga, M. Liakata, R. Procter, Learning reporting dynamics during breaking news for rumour detection in social media (2016). arXiv:1610.07363

  29. V. Qazvinian, et al., Rumor has it: identifying misinformation in microblogs, in Proceedings of the Conference on Empirical Methods in Natural Language Processing (Association for Computational Linguistics, 2011)

    Google Scholar 

  30. M. Mendoza, B. Poblete, C. Castillo, Twitter under crisis: can we trust what we RT? in Proceedings of the first workshop on social media analytics (ACM, 2010)

    Google Scholar 

  31. C. Castillo, M. Mendoza, B. Poblete, Information credibility on Twitter, in Proceedings of the 20th international conference on World wide web (ACM, 2011)

    Google Scholar 

  32. S. Kwon, et al., Prominent features of rumor propagation in online social media, in 2013 IEEE 13th International Conference on Data Mining (IEEE, 2013)

    Google Scholar 

  33. Sejeong Kwon, Meeyoung Cha, Kyomin Jung, Rumor detection over varying time windows. PLoS One 12(1), e0168344 (2017)

    Article  Google Scholar 

  34. A. Kumar, T.M. Sebastian, Sentiment analysis on Twitter. IJCSI Int. J. Comput. Sci. 9(4), 372–378 (2012)

    Google Scholar 

  35. K. Dave, S. Lawrence, D.M. Pennock, Mining the peanut gallery: opinion extraction and semantic classification of product reviews, in Proceedings of the 12th international conference on World Wide Web (ACM, 2003), pp. 519–528

    Google Scholar 

  36. A. Kumar, A. Sharma, A. Socio-sentic framework for sustainable agricultural governance. Sustain. Comput. Inform. Syst. (2018)

    Google Scholar 

  37. B. Pang, L. Lee, Opinion mining and sentiment analysis. Found. Trends Inf. Retr. J. 2(2), 1–135 (2008)

    Google Scholar 

  38. A. Kumar, T. Sebastian, Sentiment analysis: A perspective on its past, present and future. Int. J. Intell. Syst. Appl. 10, 1–14 (2012)

    Google Scholar 

  39. A. Kumar, A. Jaiswal, Empirical Study of Twitter and tumblr for sentiment analysis using soft computing techniques, in Proceedings of the World Congress on Engineering and Computer Science, vol. 1 (2017)

    Google Scholar 

  40. B. Liu, Sentiment Analysis Mining Opinions, Sentiments, and Emotions (Cambridge University Press, Chicago, 2015)

    Book  Google Scholar 

  41. A. Kumar, V. Dabas, A social media complaint workflow automation tool using sentiment intelligence, in Proceedings of The World Congress on Engineering 2016. Lecture Notes in Engineering and Computer Science (2016), pp. 176–181

    Google Scholar 

  42. A. Kumar, A. Joshi, Ontology Driven Sentiment Analysis on Social Web for Government Intelligence, in Special Collection on eGovernment Innovation in India (2017), pp. 134–139

    Google Scholar 

  43. E. Cambria, B. Schuller, Y. Xia, C. Havasi, New avenues in opinion mining and sentiment analysis. IEEE Intell. Syst. 28, 15–21 (2013)

    Article  Google Scholar 

  44. R. Feldman, Techniques and applications for sentiment analysis. Commun. ACM 56, 82–89 (2013)

    Article  Google Scholar 

  45. A. Montoyo, P. Martínez-Barco, A. Balahur, An overview of the current state of the area and envisaged developments. Decis. Support Syst. 53, 675–679 (2012)

    Article  Google Scholar 

  46. S. Finn, E. Mustafaraj, Learning to discover political activism in the Twitter verse. KI-KünstlicheIntelligenz 27, 17–24 (2013)

    Google Scholar 

  47. A. Trilla, F. Alias, Sentence-based sentiment analysis for expressive text-to-speech. IEEE Trans. Audio Speech Lang. Process. 21, 223–233 (2013)

    Article  Google Scholar 

  48. S. Tuarob, C.S. Tucker, M. Salathe, N. Ram, An ensemble heterogeneous classification methodology for discovering health-related knowledge in social media messages. J. Biomed. Inform. 49, 255–268 (2014)

    Article  Google Scholar 

  49. J. Brynielsson, F. Johansson, C. Jonsson, A. Westling, Emotion classification of social media posts for estimating people’s reactions to communicated alert messages during crises. Secur. Inform. 3, 1–11 (2014)

    Article  Google Scholar 

  50. P. Burnap, M.L. Williams, L. Sloan, O. Rana, W. Housley, A. Edwards, V. Knight, R. Procter, A. Voss, Tweeting the terror: modelling the social media reaction to the Woolwich terrorist attack. Soc. Netw. Anal. Min. 4, 1–14 (2014)

    Article  Google Scholar 

  51. A. Makazhanov, D. Rafiei, M. Waqar, Predicting political preference of Twitter users. Soc. Netw. Anal. Min. 4, 1–15 (2014)

    Article  Google Scholar 

  52. P. Bogdanov, M. Busch, J. Moehlis, A.K. Singh, B.K. Szymanski, Modeling individual topic-specific behavior and influence backbone networks in social media. Soc. Netw. Anal. Min. 4, 1–16 (2014)

    Article  Google Scholar 

  53. X. Fu, Y. Shen, Study of collective user behaviour in Twitter: a fuzzy approach. Neural Comput. Appl. 25, 1603–1614 (2014)

    Article  Google Scholar 

  54. X. Chen, M. Vorvoreanu, K. Madhavan, Mining social media data for understanding students’ learning experiences. IEEE Trans. Learn. Technol. 7, 246–259 (2014)

    Article  Google Scholar 

  55. P. Burnap, M.L. Williams, Cyber hate speech on Twitter: an application of machine classification and statistical modeling for policy and decision making. Policy Internet 7, 223–242 (2015)

    Article  Google Scholar 

  56. A. Zubiaga, D. Spina, R. Martinez, V. Fresno, Real-time classification of Twitter trends. J. Assoc. Inf. Sci. Technol. 66, 462–473 (2015)

    Article  Google Scholar 

  57. P. Andriotis, G. Oikonomou, T. Tryfonas, S. Li, Highlighting relationships of a smartphone’s social ecosystem in potentially large investigations. IEEE Trans. Cybern. 46, 1974–1985 (2016)

    Article  Google Scholar 

  58. P. Burnap, M.L. Williams, Us and them: identifying cyber hate on Twitter across multiple protected characteristics. EPJ Data Sci. 5, 1–15 (2016)

    Article  Google Scholar 

  59. N. Oliveira, P. Cortez, N. Areal, The impact of microblogging data for stock market prediction: using Twitter to predict returns, volatility, trading volume and survey sentiment indices. Expert Syst. Appl. 73, 125–144 (2017)

    Article  Google Scholar 

  60. A. Singh, N. Shukla, N. Mishra, Social media data analytics to improve supply chain management in food industries. Transp. Res. Part E Logist. Transp. Rev. 114, 398–415 (2018)

    Article  Google Scholar 

  61. H. Wang, D. Can, A. Kazemzadeh, F. Bar, S. Narayanan, A system for real-time Twitter sentiment analysis of 2012 us presidential election cycle, in Proceedings of the ACL 2012 System Demonstrations (Association for Computational Linguistics, 2012), pp. 115–120

    Google Scholar 

  62. Understanding sentiment analysis: what it is & why it’s used, https://www.brandwatch.com/blog/understanding-sentiment-analysis/. Accessed 19 Oct 2018

  63. E. Aboujaoude, M.W. Savage, V. Starcevic, W.O. Salame, Cyberbullying: review of an old problem gone viral. J. Adolesc. Health 57(1), 10–18 (2015). https://doi.org/10.1016/j.jadohealth.2015.04.011

    Article  Google Scholar 

  64. M.A. Campbell, Cyber bullying: an old problem in a new guise? J. Psychol. Couns. Sch. 15(1), 68–76 (2005)

    Google Scholar 

  65. Tokunaga Following you home from school, A critical review and synthesis of research on cyberbullying victimization. Comput. Hum. Behav. 26, 277–287 (2010). https://doi.org/10.1016/j.chb.2009.11.014

    Article  Google Scholar 

  66. Centers for Disease Control and Prevention. Youth violence: technology and youth protecting your child from electronic aggression (2014), http://www.cdc.gov/violenceprevention/pdf/ea-tipsheet-a.pdf. Accessed 11 Sept 2017

  67. P.K. Smith, J. Mahdavi, M. Carvalho, S. Fisher, S. Russell, N. Tippett, Cyberbullying: its nature and impact in secondary school pupils. J. Child Psychol. Psychiatry 49(4), 376–385 (2008). https://doi.org/10.1111/j.1469-7610.2007.01846

    Article  Google Scholar 

  68. G. Sarna, M.P. Bhatia, Content based approach to find the credibility of user in social networks: an application of cyberbullying. Int. J. Mach. Learn. Cybernet. 8(2), 677–689 (2017)

    Article  Google Scholar 

  69. All you need to know about anti-bullying laws in India, https://blog.ipleaders.in/anti-bullying-laws/ Accessed 14 July 2018

  70. Qing Li, Cyberbullying in high schools: a study of students’ behaviors and beliefs about this new phenomenon. J. Aggress. Maltreatment Trauma 19(4), 372–392 (2010). https://doi.org/10.1080/10926771003788979

    Article  Google Scholar 

  71. Qing Li, Cyberbullying in high schools: a study of students’ behaviors and beliefs about this new phenomenon. J. Aggress. Maltreatment Trauma 19(4), 372–392 (2010). https://doi.org/10.1080/10926771003788979

    Article  Google Scholar 

  72. J. Wang, T.R. Nansel, R.J. Iannotti, Cyber bullying and traditional bullying: differential association with depression. J. Adolesc. Health 48(4), 415–417 (2011)

    Article  Google Scholar 

  73. M.P. Hamm, A.S. Newton, A. Chisholm, J. Shulhan, A. Milne, P. Sundar et al., Prevalence and effect of cyberbullying on children and young people: a scoping review of social media studies. JAMA Pediatr. 169(8), 770–777 (2015). https://doi.org/10.1001/jamapediatrics.2015.0944

    Article  Google Scholar 

  74. J.A. Casas, R. Del Rey, R. Ortega-Ruiz, Bullying and cyberbullying: convergent and divergent predictor variables. Comput. Hum. Behav. 29, 580–587 (2013). https://doi.org/10.1016/j.chb.2012.11.015

    Article  Google Scholar 

  75. Commissariato di PS, Una vita da social, https://www.commissariatodips.it/uploads/media/Comunicato_stampa_Una_vita_da_social_4__edizione_2017.pdf. Accessed 28 Nov 2017

  76. Law n. 71/17 of 29/05/2017, GU n. 127 of 03/06/2017. Senatodella Repubblica, http://www.senato.it/leg/17/BGT/Schede/Ddliter/43814.htm. Accessed 11 Sept 2017

  77. Bsecure, http://www.safesearchkids.com/BSecure.html

  78. Cyber Patrol, http://www.cyberpatrol.com/cpparentalcontrols.asp

  79. eBlaster, http://www.eblaster.com/

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, Delhi Technological University, New Delhi, India

    Akshi Kumar & Saurabh Raj Sangwan

  2. Graduate School, Duy Tan University, Da Nang, Vietnam

    Anand Nayyar

Authors
  1. Akshi Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saurabh Raj Sangwan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anand Nayyar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anand Nayyar .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India

    Sudeep Tanwar

  2. Department of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Deemed University, Patiala, Punjab, India

    Sudhanshu Tyagi

  3. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Deemed University, Patiala, Punjab, India

    Neeraj Kumar

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kumar, A., Sangwan, S.R., Nayyar, A. (2020). Multimedia Social Big Data: Mining. In: Tanwar, S., Tyagi, S., Kumar, N. (eds) Multimedia Big Data Computing for IoT Applications. Intelligent Systems Reference Library, vol 163. Springer, Singapore. https://doi.org/10.1007/978-981-13-8759-3_11

Download citation

Publish with us

Policies and ethics

Search

Navigation