Skip to main content

A Literature Review on Application Areas of Social Media Analytics

  • Conference paper
  • First Online:
Business Information Systems (BIS 2019)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 354))

Included in the following conference series:

Abstract

The use of social media is part of everyday life in both private and professional environments. Social media is used for communication, data exchange and the distribution of news and advertisements. Social Media Analytics (SMA) help to collect and interpret unstructured data. The measurement of user behavior serves to form opinions and evaluate the influence of individual actors. This results in a multitude of application areas for SMA. On the basis of a literature search, our aim is to determine the main application areas and summarize the current state of research. We describe these areas, show current findings from the literature and uncover gaps in research. The main application areas of SMA investigated in research are healthcare, tourism and natural disaster control.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

References

  1. Kaplan, A.M., Haenlein, M.: Users of the world, unite! the challenges and opportunities of social media. Bus. Horiz. 53, 59–68 (2010)

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Beier, M., Wagner, K.: Social media adoption: barriers to the strategic use of social media in SMEs. In: Proceedings of the European Conference of Information Systems, pp. 1–18. AIS, Istanbul (2016)

    Google Scholar 

  4. Treem, J.W., Leonardi, P.M.: Social media use in organizations: exploring the affordances of visibility, editability, persistence, and association. Ann. Int. Commun. Assoc. 36, 143–189 (2013)

    Article  Google Scholar 

  5. El-Haddadeh, R., Weerakkody, V., Peng, J.: Social networking services adoption in corporate communication: the case of China. J. Enterp. Inf. Manage. 25, 559–575 (2012)

    Article  Google Scholar 

  6. Baars, H., Kemper, H.-G.: Management support with structured and unstructured data - an integrated business intelligence framework. Inf. Syst. Manage. 25, 132–148 (2008)

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Sinha, V., Subramanian, K.S., Bhattacharya, S., Chaudhary, K.: The contemporary framework on social media analytics as an emerging tool for behavior informatics, HR analytics and business process. Manage. J. Contemp. Manage. Issues 17, 65–84 (2012)

    Google Scholar 

  9. Kurniawati, K., Shanks, G., Bekmamedova, N.: The business impact of social media analytics. In: Proceedings of the European Conference of Information Systems, pp. 48–61. AIS, Utrecht (2013)

    Google Scholar 

  10. Khan, G.F.: Seven Layers of Social Media Analytics: Mining Business Insights from Social Media; Text, Actions, Networks, Hyperlinks, Apps, Search Engine, and Location Data. CreateSpace Independent Publishing Platform (2015)

    Google Scholar 

  11. Kataria, D.: A review on social media analytics. Int. J. Adv. Res. Ideas Innov. Technol. 3, 695–698 (2017)

    Google Scholar 

  12. Rathore, A.K., Kar, A.K., Ilavarasan, P.V.: Social media analytics: literature review and directions for future research. Decis. Anal. 14, 229–249 (2017)

    Article  MathSciNet  Google Scholar 

  13. Kotov, A.: Social media analytics for healthcare. In: Reddy, C.K., Aggarwal, C.C. (eds.) Healthcare Data Analytics. Apple Academic Press Inc. (2015)

    Google Scholar 

  14. Wang, Z., Ye, X.: Social media analytics for natural disaster management. Int. J. Geograph. Inf. Sci. 32, 49–72 (2018)

    Article  Google Scholar 

  15. Sahatiya, P.: Big data analytics on social media data: a literature review. Int. Res. J. Eng. Technol. 5, 189–192 (2018)

    Google Scholar 

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

    Article  Google Scholar 

  17. 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)

    Article  Google Scholar 

  18. Webster, J., Watson, R.T.: Analyzing the past to prepare for the future: writing a literature. Rev. MIS Q. 26, xiii–xxiii (2002)

    Google Scholar 

  19. Google Trends. https://trends.google.de/trends/explore?q=Social%20Media%20Analytics. Accessed 07 July 2018

  20. Abbasi, A., et al.: Social media analytics for smart health. IEEE Intell. Syst. 29, 60–80 (2014)

    Article  Google Scholar 

  21. Achrekar, H., Gandhe, A., Lazarus, R., Yu, S.-H., Liu, B.: Predicting flu trends using Twitter data. In: Proceedings of the IEEE Conference on Computer Communications Workshops (INFOCOM), pp. 702–707. IEEE, Shanghai (2011)

    Google Scholar 

  22. Alimova, I., Tutubalina, E.: Automated detection of adverse drug reactions from social media posts with machine learning. In: van der Aalst, Wil M.P., et al. (eds.) AIST 2017. LNCS, vol. 10716, pp. 3–15. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73013-4_1

    Chapter  Google Scholar 

  23. Akhtyamova, L., Alexandrov, M., Cardiff, J.: Review of trends in health social media analysis. In: 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies. IEEE, Yerevan (2017)

    Google Scholar 

  24. Bello-Orgaz, G., Hernandez-Castro, J., Camacho, D.: Detecting discussion communities on vaccination in Twitter. Future Gener. Comput. Syst. 66, 125–136 (2017)

    Article  Google Scholar 

  25. Chee, B.W., Berlin, R., Schatz, B.: Predicting adverse drug events from personal health messages. In: AMIA Annual Symposium Proceedings Archive, pp. 217–226 (2011)

    Google Scholar 

  26. Harpaz, R., et al.: Text mining for adverse drug events: the promise, challenges, and state of the art. Drug Saf. 37, 777–790 (2014)

    Article  Google Scholar 

  27. Jin, J., Yan, X., Li, Y., Li, Y.: How users adopt healthcare information: an empirical study of an online Q&A community. Int. J. Med. Inf. 86, 91–103 (2016)

    Article  Google Scholar 

  28. Liu, X., Chen, H.: A research framework for pharmacovigilance in health social media: identification and evaluation of patient adverse drug event reports. J. Biomed. Inf. 58, 268–279 (2015)

    Article  Google Scholar 

  29. Patki, A., Sarker, A., Pimpalkhute, P., Nikfarjam, A., Ginn, R.: Mining adverse drug reaction signals from social media: going beyond extraction. In: Proceedings of BioLinkSig, Boston, MA, USA (2014)

    Google Scholar 

  30. Ritterman, J., Osborne, M., Klein, E.: Using prediction markets and Twitter to predict a Swine Flu pandemic. In: 1st International Workshop of Mining Social Media (2009)

    Google Scholar 

  31. Sarker, A., Gonzalez, G.: Portable automatic text classification for adverse drug reaction detection via multi-corpus training. J. Biomed. Inf. 53, 196–207 (2015)

    Article  Google Scholar 

  32. Waszak, P.M., Kasprzycka-Waszak, W., Kubanek, A.: The spread of medical fake news in social media – the pilot quantitative study. Health Policy Technol. 7, 115–118 (2018)

    Article  Google Scholar 

  33. Yang, M., Li, Y., Kiang, M.: Environmental scanning for customer complaint identification in social media. In: Proceedings of the International Conference on Information Systems. AIS, Shanghai (2011)

    Google Scholar 

  34. Yang, M., Wang, X., Kiang, M.: Identification of consumer adverse drug reaction messages on social media. In: Proceedings of the 17th Pacific Asia Conference on Information Systems. AIS, Jeju Island (2013)

    Google Scholar 

  35. Yang, C.C., Yang, H.: Exploiting social media with tensor decomposition for pharmacovigilance. In: Proceedings of the IEEE International Conference on Data Mining Workshop, pp. 188–195. IEEE Computer Society, Atlantic City (2015)

    Google Scholar 

  36. Cameron, M.A., Power, R., Robinson, B., Yin, J.: Emergency situation awareness from Twitter for crisis management. In: Proceedings of the Conference on World Wide Web, pp. 695–698. ACM, New York (2012)

    Google Scholar 

  37. Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the Conference on World Wide Web, pp. 851–860. ACM, New York (2010)

    Google Scholar 

  38. Li, L., Zhang, Q., Tian, J., Wang, H.: Characterizing information propagation patterns in emergencies: a case study with Yiliang earthquake. Int. J. Inf. Manage. 38, 34–41 (2018)

    Article  Google Scholar 

  39. Cheong, F., Cheong, C.: Social media data mining: a social network analysis of tweets during the 2010–2011 Australian floods. In: Proceedings of the Pacific Asia Conference on Information Systems. AIS, Brisbane (2011)

    Google Scholar 

  40. Dong, H., Halem, M., Zhou, S.: Social Media data analytics applied to Hurricane Sandy. In: Proceedings of the International Conference on Social Computing, pp. 963–966. IEEE Computer Society, Alexandria (2013)

    Google Scholar 

  41. Verma, S.: Natural language processing to the rescue? extracting “situational awareness” tweets during mass emergency. In: Proceedings of the International AAAI Conference on Weblogs and Social Media. AAAI, Barcelona (2011)

    Google Scholar 

  42. Ross, B., Potthoff, T., Majchrzak, T.A., Chakraborty, N.R., Lazreg, M.B., Stieglitz, S.: The diffusion of crisis-related communication on social media: an empirical analysis of Facebook reactions. In: Proceedings of the Hawaii International Conference on System Sciences. AIS, Waikoloa Village (2018)

    Google Scholar 

  43. Mendoza, M., Poblete, B., Castillo, C.: Twitter under crisis: can we trust what we RT? In: Workshop on Social Media Analytics, pp. 71–79. ACM, New York (2010)

    Google Scholar 

  44. Mukkamala, A., Beck, R.: Enhancing disaster management through social media analytics to develop situation awareness what can be learned from Twitter messages about Hurricane Sandy? In: Proceedings of the Pacific Asia Conference on Information Systems. AIS, Chiayi (2016)

    Google Scholar 

  45. Vieweg, S., Hughes, A.L., Starbird, K., Palen, L.: Microblogging during two natural hazards events: what Twitter may contribute to situational awareness. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1079–1088. ACM, New York (2010)

    Google Scholar 

  46. Zin, T.T.: Knowledge based social network applications to disaster event analysis. In: Proceedings of the International MultiConference of Engineers and Computer Scientists, p. 6. IAENG, Hong Kong (2013)

    Google Scholar 

  47. Sen, A., Rudra, K., Ghosh, S.: Extracting situational awareness from microblogs during disaster events. In: Proceedings of the Communication Systems and Networks, pp. 1–6. IEEE Computer Society, Bangalore (2015)

    Google Scholar 

  48. Oh, C., Kumar, S.: How trump won: the role of social media sentiment in political elections. In: Proceedings of the Pacific Asia Conference on Information Systems, p. 48. AIS, Langkawi (2017)

    Google Scholar 

  49. Yaqub, U., Chun, S.A., Atluri, V., Vaidya, J.: Sentiment based analysis of tweets during the US presidential elections. In: Proceedings of the Annual International Conference on Digital Government Research, pp. 1–10. ACM, New York (2017)

    Google Scholar 

  50. You, Q., Cao, L., Cong, Y., Zhang, X., Luo, J.: A multifaceted approach to social multimedia-based prediction of elections. IEEE Trans. Multimedia 17, 2271–2280 (2015)

    Article  Google Scholar 

  51. Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with Twitter: what 140 characters reveal about political sentiment. In: Proceedings of the International Conference on Weblogs and Social Media. AAAI, Washington (2010)

    Google Scholar 

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

    Article  Google Scholar 

  53. David, E., Zhitomirsky-Geffet, M., Koppel, M., Uzan, H.: Utilizing Facebook pages of the political parties to automatically predict the political orientation of Facebook users. Online Inf. Rev. 40, 610–623 (2016)

    Article  Google Scholar 

  54. Agarwal, S., Sureka, A.: But I did not mean it! - intent classification of racist posts on Tumblr. In: European Intelligence and Security Informatics Conference, pp. 124–127. IEEE Computer Society, Uppsala (2016)

    Google Scholar 

  55. Stieglitz, S., Brockmann, T., Xuan, L.D.: Usage of social media for political communication. In: Proceedings of the Pacific Asia Conference on Information Systems. AIS, Ho Chi Minh City (2012)

    Google Scholar 

  56. Cheng, M., Edwards, D.: Social media in tourism: a visual analytic approach. Curr. Issues Tourism 18, 1080–1087 (2015)

    Article  Google Scholar 

  57. Park, S., Ok, C., Chae, B.: Using Twitter data for cruise tourism marketing and research. J. Tourism Mark. 33, 885–898 (2016)

    Article  Google Scholar 

  58. Marine-Roig, E., Anton Clavé, S.: Tourism analytics with massive user-generated content: a case study of Barcelona. J. Destination Mark. Manage. 4, 162–172 (2015)

    Article  Google Scholar 

  59. Xiang, Z., Schwartz, Z., Gerdes, J.H., Uysal, M.: What can big data and text analytics tell us about hotel guest experience and satisfaction? Int. J. Hospitality Manage. 44, 120–130 (2015)

    Article  Google Scholar 

  60. Chua, A., Servillo, L., Marcheggiani, E., Moere, A.V.: Mapping Cilento: using geotagged social media data to characterize tourist flows in Southern Italy. Tourism Manage. 57, 295–310 (2016)

    Article  Google Scholar 

  61. Habib, M.B., Krol, N.C.: What does Twitter tell us about tourists’ mobility behavior? a case study on tourists in The Netherlands and Belgium. In: Proceedings of the Pacific Asia Conference on Information Systems, p. 34. AIS, Langkawi (2017)

    Google Scholar 

  62. Brandt, T., Bendler, J., Neumann, D.: Social media analytics and value creation in Urban smart tourism ecosystems. Inf. Manage. 54, 703–713 (2017)

    Article  Google Scholar 

  63. Leung, X.Y., Bai, B., Stahura, K.A.: The marketing effectiveness of social media in the hotel industry: a comparison of Facebook and Twitter. J. Hospitality Tourism Res. 39, 147–169 (2015)

    Article  Google Scholar 

  64. Nann, S., Krauss, J., Schoder, D.: Predictive analytics on public data - the case of stock markets. In: Proceedings of the European Conference on Information Systems, p. 102. AIS, Utrecht (2013)

    Google Scholar 

  65. Bollen, J., Mao, H., Zeng, X.-J.: Twitter mood predicts the stock market. J. Comput. Sci. 2, 1–8 (2011)

    Article  Google Scholar 

  66. Zhang, X., Fuehres, H., Gloor, P.A.: Predicting stock market indicators through Twitter “I hope it is not as bad as I fear”. Procedia Soc. Behav. Sci. 26, 55–62 (2011)

    Article  Google Scholar 

  67. Vu, T.T., Chang, S., Ha, Q.T., Collier, N.: An experiment in integrating sentiment features for tech stock prediction in Twitter. In: Proceedings of the Workshop on Information Extraction and Entity Analytics on Social Media Data, pp. 23–38. The COLING 2012 Organizing Committee, Mumbai (2012)

    Google Scholar 

  68. Adamopoulos, P., Todri, V.: Social media analytics: the effectiveness of promotional events on brand user base in social media. In: Proceedings of the International Conference on Information Systems. AIS, Auckland (2014)

    Google Scholar 

  69. Bekmamedova, N., Shanks, G.: Social media analytics and business value: a theoretical framework and case study. In: Proceedings of the Hawaii International Conference on System Sciences, pp. 3728–3737. IEEE Computer Society, Waikoloa (2014)

    Google Scholar 

  70. Ribarsky, W., Xiaoyu Wang, D., Dou, W.: Social media analytics for competitive advantage. Comput. Graph. 38, 328–331 (2014)

    Article  Google Scholar 

  71. Seebach, C., Beck, R., Denisova, O.: Sensing social media for corporate reputation management: a business agility perspective. In: Proceedings of the European Conference on Information Systems. AIS, Barcelona (2012)

    Google Scholar 

  72. Davcheva, P.: Identifying sports talents by social media mining as a marketing instrument. In: Annual SRII Global Conference, pp. 223–227. IEEE Computer Society, San Jose (2014)

    Google Scholar 

  73. He, W., Wang, F.-K., Zha, S.: Enhancing social media competitiveness of small businesses: insights from small pizzerias. New Rev. Hypermedia Multi. 20, 225–250 (2014)

    Article  Google Scholar 

  74. Rathore, A.K., Ilavarasan, P.V.: Social media analytics for new product development: case of a pizza. In: Proceedings of the International Conference on Advances in Mechanical, Industrial, Automation and Management Systems, pp. 213–219. IEEE, Allahabad (2017)

    Google Scholar 

  75. Misopoulos, F., Mitic, M., Kapoulas, A., Karapiperis, C.: Uncovering customer service experiences with Twitter: the case of airline industry. Manage. Decis. 52, 705–723 (2014)

    Article  Google Scholar 

  76. He, W., Zha, S., Li, L.: Social media competitive analysis and text mining: a case study in the pizza industry. Int. J. Inf. Manage. 33, 464–472 (2013)

    Article  Google Scholar 

  77. Ko, N., Jeong, B., Choi, S., Yoon, J.: Identifying product opportunities using social media mining: application of topic modeling and chance discovery theory. IEEE Access 6, 1680–1693 (2018)

    Article  Google Scholar 

  78. Su, C.J., Chen, Y.A.: Social media analytics based product improvement framework. In: International Symposium on Computer, Consumer and Control, pp. 393–396. IEEE Computer Society, Xi’an (2016)

    Google Scholar 

  79. Mirbabaie, M., Stieglitz, S., Eiro, M.R.: #IronyOff – understanding the usage of irony on Twitter during a corporate crisis. In: Proceedings of the Pacific Asia Conference on Information Systems, p. 66. AIS, Langkawi (2017)

    Google Scholar 

  80. Stieglitz, S., Mirbabaie, M., Potthoff, T.: Crisis communication on Twitter during a global crisis of volkswagen - the case of “Dieselgate.” In: Proceedings of the Hawaii International Conference on System Sciences. AIS, Waikoloa (2018)

    Google Scholar 

  81. Melville, P., Sindhwani, V., Lawrence, R.D.: Social media analytics: channeling the power of the blogosphere for marketing insight. Proc. WIN 1, 1–5 (2009)

    Google Scholar 

  82. Oh, C., Yergeau, S., Woo, Y., Wurtsmith, B., Vaughn, S.: Is Twitter psychic? social media analytics and television ratings. In: Proceedings of the International Conference on Computing Technology and Information Management, pp. 150–155 (2015)

    Google Scholar 

  83. Pensa, R.G., Sapino, M.L., Schifanella, C., Vignaroli, L.: Leveraging cross-domain social media analytics to understand TV topics popularity. IEEE Comput. Intell. Mag. 11, 10–21 (2016)

    Article  Google Scholar 

  84. Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. J. Econ. Persp. 31, 211–236 (2017)

    Article  Google Scholar 

  85. Xiang, Z., Du, Q., Ma, Y., Fan, W.: A comparative analysis of major online review platforms: implications for social media analytics in hospitality and tourism. Tourism Manage. 58, 51–65 (2017)

    Article  Google Scholar 

  86. Aswani, R., Kar, A.K., Vigneswara Ilavarasan, P.: Detection of spammers in Twitter marketing: a hybrid approach using social media analytics and bio inspired computing. Inf. Syst. Frontiers 20, 515–530 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to León Gilhaus .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liere-Netheler, K., Gilhaus, L., Vogelsang, K., Hoppe, U. (2019). A Literature Review on Application Areas of Social Media Analytics. In: Abramowicz, W., Corchuelo, R. (eds) Business Information Systems. BIS 2019. Lecture Notes in Business Information Processing, vol 354. Springer, Cham. https://doi.org/10.1007/978-3-030-20482-2_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-20482-2_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20481-5

  • Online ISBN: 978-3-030-20482-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics