Advertisement

Social Media Data—A Glorious Mess

  • Stefan StieglitzEmail author
Chapter

Abstract

Social media became a mass media that is used by organizations, private persons, journalists, and US presidents. This opens up great potentials to share and disseminate information on different platforms and in various formats. At the beginning of this development some researchers expressed hope (and still do) that this could be a chance for useful public multi-directional communication between citizens and politicians as well as between consumers and companies. Higher transparency, consensus building, sharing of personal experiences are examples for positive expectations towards social media. At the same time, it is becoming obvious that negative effects such as the spreading of fake news and rumours or manipulation of people by social bots and echo chambers are serious challenges for organizations, people, and society. What can researchers do to contribute to this problem? We need to be able to collect and analyse communication in order to understand the underlying principles and to derive solutions that lower the dark sides of social media. Structuring social media data is one of the most important steps in this context in order to increase transparency and prevent misuse. This article explains what it actually is what we need to structure, why it is relevant to structure and how we can do it.

Keywords

Social media Data Analytics Structure 

References

  1. Carlsson, S. A., Zafeiropoulou, S., & Sarker, S. (2015). What’s trending in social media analytics area? A retrospective. In Twenty-first Americas Conference on Information Systems (pp. 1–15).Google Scholar
  2. Forestier, M., Stavrianou, A., Velcin, J., & Zighed, D. A. (2012). Roles in social networks: Methodologies and research issues. Web Intelligence and Agent Systems.Google Scholar
  3. Fritz, C. E., & Matthewson, J. H. (1957). Convergence behavior in disasters: A problem in social control. American sociological review.Google Scholar
  4. Holsapple, C., Hsiao, S. H., & Pakath, R. (2014). Business social media analytics: Definition, benefits, and challenges. Completed research paper. In 20th Americas Conference on Information Systems, AMCIS 2014 (pp. 1–12).Google Scholar
  5. Kurniawati, K., Shanks, G., & Bekmamedova, N. (2013). The business impact of social media analytics. In ECIS (p. 48).Google Scholar
  6. Oh, O., Agrawal, M., & Rao, H. R. (2013). Community intelligence and social media services: A rumor theoretic analysis of tweets during social crises. Management Information Systems Quarterly, 37, 407–426.CrossRefGoogle Scholar
  7. Stieglitz, S., Dang-Xuan, L., Bruns, A., & Neuberger, C. (2014). Social media analytics. Business and Information Systems Engineering.Google Scholar
  8. Stieglitz, S., Mirbabaie, M., Ross, B., & Neuberger, C. (2018). Social media analytics—challenges in topic discovery, data collection, and data preparation. International Journal of Information Management, 39, 156–168.CrossRefGoogle Scholar
  9. vom Brocke, J., Becker, J., Braccini, A. M., Butleris, R., Hofreiter, B., Kęstutis, K., … Tomáš, S. (2011). Current and future issues in BPM research: A European perspective from the ERCIS meeting 2010. Communications of the AIS.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of Duisburg-EssenDuisburgGermany

Personalised recommendations