DIGIB201-21B (HAM)

Creating Value with Social Media Analytics

15 Points

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Division of Management
School of Management and Marketing


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: ashleigh.neame@waikato.ac.nz
: sade.lomas@waikato.ac.nz

Placement/WIL Coordinator(s)


: hana.bouafif@waikato.ac.nz
: hannah.bintirajaazlan@waikato.ac.nz
: shikha.kumar@waikato.ac.nz
: mike.lim@waikato.ac.nz

Student Representative(s)

Lab Technician(s)


: yilan.chen@waikato.ac.nz

You can contact staff by:

  • Calling +64 7 838 4466 select option 1, then enter the extension.
  • Extensions starting with 4, 5, 9 or 3 can also be direct dialled:
    • For extensions starting with 4: dial +64 7 838 extension.
    • For extensions starting with 5: dial +64 7 858 extension.
    • For extensions starting with 9: dial +64 7 837 extension.
    • For extensions starting with 3: dial +64 7 2620 + the last 3 digits of the extension e.g. 3123 = +64 7 262 0123.
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Paper Description

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This comprehensive paper explores conceptual, managerial, and technical issues surrounding the use of social media data to enhance business decision making. Although the course is data-intensive, yet it is non-technical and suits business managers, experts, and management students. This paper uses lectures, lab-based activities, and investigations of real organisations to examine conceptual, managerial, and technical issues surrounding social media analytics in organizations. Assignments will include the application of tools and technologies to given tasks, analysis, and writing of results on small projects and a larger final project that integrates the types of insights and analysis learned in class to study a specific type of social media.

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Paper Structure

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With instructor-led lectures, individual assignments, and lab-based tutorials, this paper will equip you with knowledge and tools to extract, manage, and analyse a variety of social media, data including text data (such as comments and reviews), customer networks, search engine data, locations data, and multimedia data. In addition, this paper features a group project requiring you to solve a business or social problem using social med data.

Mode of Delivery

This paper is delivered in FLEXI mode meaning that your learning can be done face-to-face (on campus) or online. This gives you flexibility as to where and how you learn during the trimester. You are required to specify your default learning approach (face-to-face (on campus) or online) by the end of week one. Further information on how to do that will be available on Moodle.

This paper will be delivered via a combination of lectures and in-lab tutorials. All course materials will be available on Moodle. All lectures will be recorded and uploaded on Moodle each week. Furthermore, instead of in-class participation, 10 online activities will be assigned throughout the trimester in Moodle. Each activity will be live on Moodle for 5 days.

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Learning Outcomes

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Students who successfully complete the paper should be able to:

  • Have an in-depth understanding of social media and analytics technologies and platforms.
    Linked to the following assessments:
    Online Learning Activities (1)
  • Develop analytical and critical thinking skills to evaluate social media and analytics related issues faced in business and professional careers.
    Linked to the following assessments:
  • Understand the role of social media data in business decision making.
    Linked to the following assessments:
    Network Analysis (individual) (12)
    Group Project (13)
  • Possess a well-grounded understanding of different types of social media data including text, actions, apps, networks, hyperlinks, search engines, and geoloc
    Linked to the following assessments:
    Tutorial 4: Network Analytics (5)
    Tutorial 5: Text Analytics (6)
    Tutorial 6: Think with Google Tools (7)
    Tutorial 7: Location mapping with Esri (8)
    Tutorial 8: Website Analytics with Google Analytics (9)
    Tutorial 9: Mobile Analytics (10)
    Tutorial 10: Multimedia Analytics (11)
  • Analyse business situations and propose an effective social media strategy for an organisation across such disparate areas as IT, customer service, sales, and communications
    Linked to the following assessments:
    Online Learning Activities (1)
    Tutorial 1: Social Media Maturity Assessment (2)
    Tutorial 3: Social Media Risk Assessment (4)
  • Understand social media analytics business alignment and develop analytical and critical thinking of social media security, ethics, and privacy issues.
    Linked to the following assessments:
    Online Learning Activities (1)
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Assessment Components

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The internal assessment/exam ratio (as stated in the University Calendar) is 100:0. There is no final exam. The final exam makes up 0% of the overall mark.

The internal assessment/exam ratio (as stated in the University Calendar) is 100:0 or 0:0, whichever is more favourable for the student. The final exam makes up either 0% or 0% of the overall mark.

Component DescriptionDue Date TimePercentage of overall markSubmission MethodCompulsory
1. Online Learning Activities
  • Online: Submit through Moodle
2. Tutorial 1: Social Media Maturity Assessment
  • Online: Submit through Moodle
3. Tutorial 2: Creating Twitter AC and Blog
  • Online: Submit through Moodle
4. Tutorial 3: Social Media Risk Assessment
  • Online: Submit through Moodle
5. Tutorial 4: Network Analytics
  • Online: Submit through Moodle
6. Tutorial 5: Text Analytics
  • Online: Submit through Moodle
7. Tutorial 6: Think with Google Tools
  • Online: Submit through Moodle
8. Tutorial 7: Location mapping with Esri
  • Online: Submit through Moodle
9. Tutorial 8: Website Analytics with Google Analytics
  • Online: Submit through Moodle
10. Tutorial 9: Mobile Analytics
  • Online: Submit through Moodle
11. Tutorial 10: Multimedia Analytics
  • Online: Submit through Moodle
12. Network Analysis (individual)
12 Sep 2021
11:30 PM
  • Online: Submit through Moodle
13. Group Project
5 Nov 2021
11:30 PM
  • Online: Submit through Moodle
Assessment Total:     100    
Failing to complete a compulsory assessment component of a paper will result in an IC grade
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Required and Recommended Readings

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Required Readings

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Creating Value With Social Media Analytics: Managing, Aligning, and Mining Social Media Text, Networks, Actions, Location, Apps, Hyperlinks, Multimedia, & Search Engines Data by Gohar F. Khan, 2018, ISBN: 1977543979.
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Recommended Readings

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Other Recommended

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Other Resources

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Further readings

Week 2

Akter, S., Bhattacharyya, M., Wamba, S. F., & Aditya, S. (2016). How does Social Media Analytics Create Value?.Journal of Organizational and End User Computing (JOEUC), 28(3), 1-9. doi:10.4018/JOEUC.2016070101

Bekmamedova N., and Shanks, G. "Social Media Analytics and Business Value: A Theoretical Framework and Case Study,"2014 47th Hawaii International Conference on System Sciences, Waikoloa, HI, 2014, pp. 3728-3737.

Andreas M. Kaplan, Michael Haenlein, (2010), Users of the world, unite! The challenges and opportunities of Social Media, Business Horizons, Volume 53, Issue 1, January–February 2010, Pages 59-68, ISSN 0007-6813, https://doi.org/10.1016/j.bushor.2009.09.003. (http://www.sciencedirect.com/science/article/pii/S0007681309001232)

Boyd, d. m. and N. B. Ellison (2007). "Social Network Sites: Definition, History, and Scholarship." Journal of Computer-Mediated Communication13(1): 210-230.

Jan H. Kietzmann, Kristopher Hermkens, Ian P. McCarthy, Bruno S. Silvestre, Social media? Get serious! Understanding the functional building blocks of social media, Business Horizons, Volume 54, Issue 3, 2011, Pages (http://www.sciencedirect.com/science/article/pii/S0007681311000061)

Week 3

Fan, W., & Gordon, M. D. (2014). The power of social media analytics. Commun. ACM, 57(6), 74-81. 10.1145/2602574

Chen, H., C. R.H.L., et al.(2012). "Business Intelligence and Analytics: From Big Data to Big Impact."MIS Quarterly36 (4): 1165-1188.

Lustig, I., B. Dietrich, et al. (2010) "The Analytics Journey: An IBM view of the structured data analysis landscape: descriptive, predictive and prescriptive analytics."Analytics-Magazine, available at:http://www.analytics-magazine.org/november-december-2010/54-the-analytics-journey

Ben Davis, 30 brands with excellent social media strategies, available at:https://econsultancy.com/blog/68167-30-brands-with-excellent-social-media-strategies/ By Ben Davis @ Econsultancy

Week 4

Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment? International Journal of Production Economics, 182, 113-131. doi:https://doi.org/10.1016/j.ijpe.2016.08.018

Henderson, J. C. and N. Venkatraman (1993). "Strategic alignment: leveraging information technology for transforming organizations."IBM Syst. J.32(1): 4-16.

Ransbotham, S. (2015) "Once You Align the Analytical Stars, What's Next?".

John Ladley (2016), Business alignment techniques for successful and sustainable analytics, https://www.cio.com.

Week 5

Debatin, B., Lovejoy, J. P., Horn, A.-K. and Hughes, B. N. (2009), Facebook and Online Privacy: Attitudes, Behaviors, and Unintended Consequences. Journal of Computer-Mediated Communication, 15: 83–108. doi:10.1111/j.1083-6101.2009.01494.x

Pekka, A. (2010). Social media, reputation risk and ambient publicity management. Strategy & Leadership, 38(6), 43-49. 10.1108/10878571011088069

Wu He,(2012)"A review of social media security risks and mitigation techniques",Journal of Systems and Information Technology,Vol. 14Issue: 2,pp.171-180,https://doi.org/10.1108/13287261211232180

Wu He, (2013) "A survey of security risks of mobile social media through blog mining and an extensive literature search",Information Management & Computer Security, Vol. 21 Issue: 5, pp.381-400,https://doi.org/10.1108/IMCS-12-2012-0068

Ajami, R., Qirim, N. A., & Ramadan, N. (2012). Privacy Issues in Mobile Social Networks. Procedia Computer Science, 10, 672-679. http://dx.doi.org/10.1016/j.procs.2012.06.086

Week 6

Khan, G. F., Yoon, H. Y., & Park, H. W. (2014). Social media communication strategies of government agencies: Twitter use in Korea and the USA. Asian Journal of Communication, 24(1), 60-78. 10.1080/01292986.2013.851723

Khan G. F., Jacob W.,”Knowledge Networks of the Information Technology Management Domain: A Social Network Analysis Approach,”Communications of the Association for Information Systems: Vol. 39, Article 18.

Steketee, M., Miyaoka, A., & Spiegelman, M. (2015). Social Network Analysis A2 - Wright, James D International Encyclopedia of the Social & Behavioral Sciences (Second Edition) (pp. 461-467). Oxford: Elsevier.

Monaghan, S., Lavelle, J., & Gunnigle, P. (2017). Mapping networks: Exploring the utility of social network analysis in management research and practice. Journal of Business Research, 76, 136-144. https://doi.org/10.1016/j.jbusres.2017.03.020

Week 7

Berezina, K., Bilgihan, A., Cobanoglu, C., & Okumus, F. (2016). Understanding Satisfied and Dissatisfied Hotel Customers: Text Mining of Online Hotel Reviews. Journal of Hospitality Marketing & Management, 25(1), 1-24. 10.1080/19368623.2015.983631

Mostafa, M. M. (2013). More than words: Social networks’ text mining for consumer brand sentiments. Expert Systems with Applications, 40(10), 4241-4251. http://dx.doi.org/10.1016/j.eswa.2013.01.019

He, W., Zha, S., & Li, L. (2013). Social media competitive analysis and text mining: A case study in the pizza industry. International Journal of Information Management, 33(3), 464-472. http://dx.doi.org/10.1016/j.ijinfomgt.2013.01.001

Chakraborty, G., M. Pagolu, et al. (2013).Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS, SAS Institute.

Shulman, S. (2014). "Five Pillars of Text Analytics, available at:http://www.screencast.com."

Week 8

CHOI, H. and VARIAN, H. (2012), Predicting the Present with Google Trends. Economic Record, 88: 2–9. doi:10.1111/j.1475-4932.2012.00809.x

Zhang, S., & Cabage, N. (2017). Search engine optimization: Comparison of link building and social sharing.The Journal of Computer Information Systems,57(2), 148-159.

Iredale S., Heinze A. (2016) Ethics and Professional Intimacy Within the Search Engine Optimisation (SEO) Industry. In: Kreps D., Fletcher G., Griffiths M. (eds) Technology and Intimacy: Choice or Coercion. HCC 2016. IFIP Advances in Information and Communication Technology, vol 474. Springer, Cham

Yang, X., Pan, B., Evans, J. A., & Lv, B. (2015). Forecasting Chinese tourist volume with search engine data. Tourism Management, 46, 386-397. http://dx.doi.org/10.1016/j.tourman.2014.07.019

Week 9

Agarwal, A., K. Hosanagar, et al.(2011). "Location, Location, Location: An Analysis of Profitability of Position in Online Advertising Markets." Journal of Marketing Research 48(6): 1057-1073.

Wu, C., Ren, F., Wan, Y., Ning, P., Du, Q., & Ye, X. (2016). Spatial and social media data analytics of housing prices in Shenzhen, China.PLoS ONE,11(10).

Garber, L. (2013). Analytics goes on location with new approaches.Computer,46(4), 14-17.

He, W., Tian, X., Chen, Y., & Chong, D. (2016). Actionable Social Media Competitive Analytics For Understanding Customer Experiences. Journal of Computer Information Systems, 56(2), 145-155. 10.1080/08874417.2016.1117377

Chan, H. K., Lacka, E., Yee, R. W. Y., & Lim, M. K. (2017). The role of social media data in operations and production management. International Journal of Production Research, 55(17), 5027-5036. 10.1080/00207543.2015.1053998

Week 10

Tabatha A. Farney, 2011, Click Analytics: Visualizing Website Use Data, Information Technology and Libraries, 01 September 2011, Vol.30(3)

Plaza, B. (2011). Google Analytics for measuring website performance. Tourism Management, 32(3), 477-481. http://dx.doi.org/10.1016/j.tourman.2010.03.015

Pakkala, H., Presser, K., & Christensen, T. (2012). Using Google Analytics to measure visitor statistics: The case of food composition websites. International Journal of Information Management, 32(6), 504-512. http://dx.doi.org/10.1016/j.ijinfomgt.2012.04.008

Week 11

Khan, G. F. and S. Vong (2014). "Virality over YouTube: an empirical analysis." Internet Research 24(5): 629-647.

Park, H. W. (2003). "Hyperlink network analysis: A new method for the study of social structure on the web."Connections 25 (49-61).

Minch, R. P. (2004). Privacy Issues in Location-Aware Mobile Devices. the 37th Hawaii International Conference on System Sciences, Big Island, HI, USA.

Brandt, T., Bendler, J., & Neumann, D. Social media analytics and value creation in urban smart tourism ecosystems. Information & Managementhttps://doi.org/10.1016/j.im.2017.01.004

Ackland, R. (2010). WWW Hyperlink Networks. Analyzing Social Media Networks with NodeXL: Insights from a connected world.D. Hansen, B. Shneiderm and K. H. M. Smith, Morgan-Kaufmann.

Week 12

Karim, A., Noushad, K., Khan, G. F., (2016), Social media Analytics Capability Framework, proceedings of 20th Pacific Asia Conference on Information Systems (PACIS) 2016, Taiwan.

Audio Analytic, L. (2017). https://www.audioanalytic.com/about-us/.

Bruni, L., Francalanci, C., & Giacomazzi, P. (2012). The Role of Multimedia Content in Determining the Virality of Social Media Information. Information & Management, 3, 278-289.

Gagvani, N. (2008). Introduction to video analytics, avialable at: https://www.eetimes.com/document.asp?doc_id=1273834.

Huddy, G. (2017). What is image analysis: how brands can use image analysis for brand insights, https://www.crimsonhexagon.com/blog/what-is-image-analysis/.

Marder, M., Harary, S., Ribak, A., Tzur, Y., Alpert, S., & Tzadok, A. (2015). Using image analytics to monitor retail store shelves. IBM Journal of Research and Development, 59(2/3), 3:1-3:11. doi:10.1147/JRD.2015.2394513

Tools and resources

Twitter Tools

Twitter Analytical Applications (no programming skills required)

Twitter Help Resources

Facebook Tools

Applications (no programing skills required)

YouTube Tools

Applications (no programming skills required)

Data Resources

  • Pajek (a social network analysis software)sample datasets(note that Pajek network data can be imported into NodeXL).
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Online Support

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Web Support is available via Moodle. Simply log in and select DIGIB201-20B (HAM) Social Media Analytics. Moodle will contain copies of the slides used in class, useful team resources, as well as details of assignments and assessment schedules (these will be posted on the Moodle at the appropriate times in the course).
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Expected workload is approximately 150 hours in total.
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Linkages to Other Papers

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Restricted papers: MSYS353, MSYS453 and DIGIB301

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