Social Analytics – An Overview

Social Media Analytics can be defined as the measurement, monitoring and analysis generated based on data gathered from social media websites. The retrieved data is then translated into meaningful reports in order to understand and optimize business resources and decisions. Social media analytics is concerned with developing and evaluating informatics tools and frameworks to collect, monitor, analyze, summarize, and visualize social media data, usually driven by specific requirements from a target application [ZCLL18].

The number of Internet, social media, and mobile users tripled over the past decade, reaching a third of the world’s population [BC02]. According to Facebook, the social network had 1.11 billion active accounts on May 2013 and the number does not stop increasing. Moreover Twitter users create more than 1 billion tweets each week, generating nearly 500 gigabytes of information every month per user, which is equivalent to 500 Encyclopedia Britannicas. The magnitude of readily available data is staggering, and while it may have been initially generated by younger members of the population, closer examination of this activity indicates that much of the current United States population is employing social media today [MT10].

The raise of searches containing ‘Social Media’ in 2009 is followed by the raise of searches about ‘Social Media Marketing’, and further on the rise of ‘Social Media Strategy’, which encloses social media analytics, tracking and monitoring.



Because social media is already a critical part of the information ecosystem and as social media platforms and applications gain widespread adoption with unprecedented reach to users, consumers, voters, businesses, governments, and nonprofit organizations alike, interest in social media from all walks of life has been skyrocketing from both application and research perspectives [ZCLL18].

Monitoring and analysis are critical steps for success regarding the strategic use of Social Media. Tracking the pulse of the social media outlets, enables companies to gain feedback and insight in how to improve and market products better [LJ09]. Measurement is also important to determine ROI. As managers are more comfortable with including blogs and social networks as part of their integrated marketing communications, they have naturally turned their attention to questions regarding the return on investment of social media [HF07].

Research on social media has greatly intensified in the past few years given the significant interest from the application’s perspective and the associated unique technical and social science challenges and opportunities [ZCLL18]. Among the purposes to invest in social media analytics research we can highlight:

  • Improvement of information flow, so on facilitating the interaction between online and offline communities;
  • Extraction of useful patterns and intelligence to serve entities that include, but are not limited to, active contributors in ongoing dialogues [ZCLL18];
  • Increase of productivity and optimization of resources not only for marketing purposes, but reaching the whole business model;
  • Improvement of quality and enhancement of creativity and innovation by generating content and products with a more personalized approach;
  • Identification of new target strategies based on audience consumption behavior;

For marketing purposes, it could be argued that automated monitoring is a more objective tool for the measurement and analysis of word-of-mouth than surveys and interviews, because when asked for an opinion, people tend to construct it more than they might when not observed [TJK15]. As social media continues to play an increasingly important role in online engagement, marketers will increasingly require high quality measurement of the channel’s impact on consumer behavior [CS03]

Social media analytics helps to identify meaningful topics, demographic information, and other relevant knowledge that is important to the general public. Solutions to the challenge of this knowledge discovery from the magnitude of social media data must be automated and sophisticated enough to detect sentiment, trends, influencers, and real time issues as they arise [MT10]. The key for Social Media Analytics is “transforming” complex data sets extracted from different social networks into information by using understandable and accessible formats.



[A01] Abbasi, A. (2012) Social Media Analytics. University of Virginia. Retrieved on October 21, 2012 from
[BC02] Booz & Company. (2011). Web and Social Media Analytics A Data and Technology Perspective. Retrieved on October 18, 2013, from .
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[CS04] Comscore. (June, 2012). The Power of Like 2 – How Marketing Works. Retrieved on October 21, 2012, from .
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[HF07] Hoffman, D. L., Fodor, M. (2010) Can you measure the ROI of your Social Media Marketing?. MIT Sloan Management Review, 52(1), 41-49.
[KH08] Kaplan, A. M., Haenlein, M. (2010) Users of the World, unite! The Challenges and Opportunities of Social Media. Business Horizons, 53, 59-68.
[LJ09] Leskovec, J. (2011). Social Media Analytics – Tracking, Modeling and Predicting the Flow of Infortmation through Networks. Stanford University.  Retrieved on October 16, 2013 from
[MT10] MicroTech. (December 2011). Social Media Analytics How It Can Help Shape Government Performance. Retrieved on October 18, 2013, from .
[MB11] Millward Brown. (May 2012). How social technologies drive business success. Retrieved on October 21, 2012, from .
[MC12] Murdough, C. (2009) Social Media Measurement: It’s Not Impossible. Journal or Interactive Adversitising, 10(1).
[SJ13] Sterne, J. (2011) Social Media Metrics: How to Measure and Optimize Your Marketing Investment.
[SM14] Sponder, M. (2012) Social Media Analytics – Effective Tools for Building, Interpreting and Using Metrics.MC Graw Hill.
[TJK15] Töllinen, A., Järvinen, J., & Karjaluoto, H. (2012). Social Media Monitoring in the Industrial Business to Business Sector. World Journal of Social Sciences, 2 (4), 65-76. Retrieved on October 21, 2013, from .
[VE16] Vertica. (May, 2012). Leveraging Social Media Analytics for Competitive Advantage. Retrieved on October 18, 2013, from .
[WRE17] Withaar, Robin J. and Ribeiro, Gabriella F. and Effing, Robin (2013) The Social Media Indicator 2: Towards a Software Tool for Measuring the Influence of Social Media. In: Informatik 2013 – Informatiek angepasst an Mensch, Organisation und Umwelt, 43. Jahrestagung der Gesellschaft fur Informatik, 16. bis 20. September 2013, Koblenz.
[ZCLL18] Zeng, D., Chen, H., Lusch, R., & Li, S. (November/December 2010). Social Media Analytics and Intelligence. IEEE Computer Society, 25(6), 13-16.

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