DIGIB201-18B (HAM)

Social Media Analytics

15 Points

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Waikato Management School
Te Raupapa
School of Management and Marketing


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: helena.wang@waikato.ac.nz
: lori.jervis@waikato.ac.nz
: sade.lomas@waikato.ac.nz

Placement Coordinator(s)


: samera.noorzai@waikato.ac.nz
: muhammad.zahid@waikato.ac.nz

Student Representative(s)

Lab Technician(s)


You can contact staff by:

  • Calling +64 7 838 4466 select option 1, then enter the extension.
  • Extensions starting with 4, 5 or 9 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.
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Paper Description

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This paper uses lectures, lab-based activities, and investigations of real organisations to examine conceptual, managerial, ad technical issues surrounding social media use 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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The format for classroom sessions is a combination of instructor and student-led presentations that utilize social media and other productivity tools to discuss relevant questions, analyse cases, and provide the insights that will help you 'scaffold' your own learning. In addition, this paper features several hands-on tutorials designed to help you extract, analyze, and visualize a variety of social media data to enhance business decision making. The course is non-technical in nature and suits business managers, experts, and management students.

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Learning Outcomes

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Students who successfully complete the course should be able to:

  • 1. Have an in-depth understanding of social media and smart technologies and platforms. 2. Develop analytical and critical thinking skills to evaluate social media smart technologies related issues faced in business and professional careers.
    Linked to the following assessments:
  • 3. Understand the role of social media data and smart technolgoes in business decision making 4. 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:
  • • 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:
  • 6. Understand social media analytics business alignment 7. Develop analytical and critical thinking of social media security, ethics, and privacy issues.
    Linked to the following assessments:
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Group Project

The group project will be graded based on the following criteria:

  1. Is the team focused on interesting problem/question? (20%)
  2. To what extent the team succeeds in solving (elaborating) the issue with social media data? (30%)
  3. Is the paper theoretically and methodologically sound? (20%)
  4. Does the paper include a meaningful visualization of the data (20%)
  5. Writing composition (grammar, spelling, logic, and ease of reading) (10%)

Network Analysis

The network analysis project will be graded based on the following criteria:

  1. The report includes a meaningful visualization of the network. You should filter the network, for example, highlighting the important nodes, showing the intensity of collaboration (e.g., with links width and colors) (5 points).
  2. The report includes network-level statistics (such as total number nodes, clustering coefficient, average degree, density, and diameter) with a brief explanation related to these statistics. What do these statistics say about the nature of the network? Is it a scale-free or small world network? (2.5 points).
  3. The report includes a list of top 10 nodes (institutions) in terms of degree, betweenness, and eigenvector centralities accompanied by a brief explanation related to the statistics. Provide a meaningful description of who the institutions are, what their role is in the network, and how their research and teaching activities related to that role, etc (5 points).
  4. In terms of Eigenvector Centrality, what is the position of the Waikato University as compared to others and what does it mean? (2.5 points).

Quizzes will be marked based on the following criteria.

Test Criteria050607080100


Concisely and clearly written

Rambling, some irrelevancies and errors, incomplete statements

Reasonably succinct, simple and understandable

Succinct and poignant, clear and grammatically correct


Mastery of concepts
(WHAT is this?)

Limited evidence of conceptual understanding, description often incorrect

Reasonable coverage of concepts, description not completely correct

Complete comprehension demonstrated,
description correct

Depth of argument, justification and illustration
(WHY is it this?)

List of points or sweeping statements without justification, not linked to practice or illustrated by examples

Relevant, reasoned argument, justified through practice and/or example

Points and statements fully address main question, comprehensive justification illustrated through appropriate evidence

Computer Practicals
Each practical has its own specific marking guide, with a more general rubric as below. If you have any questions concerning the practicals, email the tutors.

PRACTICALS Criteria050 6070 80100


Concisely and clearly written

Rambling, some irrelevancies and errors, incomplete statements

Reasonably succinct, simple and understandable

Succinct and poignant, clear and grammatically correct


Mastery of concepts/ demonstration of technical skill
(WHAT is it?)

Limited evidence of conceptual understanding, description often incorrect, technical skill not demonstrated

Reasonable coverage of concepts, description not completely correct, some technical skill demonstrated

Complete comprehension shown, description correct, mastery of technical skill demonstrated

(WHY is it this?)

No justification, not linked to practice or illustrated by examples

Some justification, some links to practice and/or illustrated through examples

Comprehensive justification illustrated through links to practice and/or examples

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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. Group Project
26 Oct 2018
11:30 PM
  • Online: Submit through Moodle
2. Network Analysis (individual)
14 Sep 2018
11:30 PM
  • Online: Submit through Moodle
3. Tutorial 1: Social Media Maturity Assessment
10 Aug 2018
11:30 PM
  • Online: Submit through Moodle
4. Tutorial 2: Creating Twitter AC and Blog
17 Aug 2018
11:30 PM
  • Online: Submit through Moodle
5. Tutorial 3: Social Media Risk Assessment
24 Aug 2018
11:30 PM
  • Online: Submit through Moodle
6. Tutorial 4: Network Analytics with Gephi
14 Sep 2018
11:30 PM
  • Online: Submit through Moodle
7. Tutorial 5: Text Analytics with IBM Watson
21 Sep 2018
11:30 PM
  • Online: Submit through Moodle
8. Tutorial 6: Google Trends & Correlate Analysis
28 Sep 2018
11:30 PM
  • Online: Submit through Moodle
9. Tutorial 7: Location mapping with Esri
5 Oct 2018
11:30 PM
  • Online: Submit through Moodle
10. Tutorial 8: Website Analytics with Google Analytics
12 Oct 2018
11:30 PM
  • Online: Submit through Moodle
11. Quiz 1
7 Sep 2018
9:00 AM
  • Hand-in: In Lecture
12. Quiz 2
12 Oct 2018
9:00 AM
  • Hand-in: In Lecture
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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Required: (You will need to purchase a copy of the following text)

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

'Social Media Marketing', by Tuten, T. and Solomon, M., Pearson Prentice Hall, 2012.

'The Social Media Management Handbook: Everything You Need To Know To Get Social Media Working In Your Business', by Smith, N., Wollan, R. and Zhou, C., John Wiley and Sons, 2011.

Smith, R. D. (2013). Strategic Planning for Public Relations. New York; London: Routledge.

Social Media Metrics for Dummies by Leslie Poston ISBN-13: 978-1118027752

Social Media ROI: Managing and Measuring Social Media Efforts in Your Organization by Olivier Blanchard ISBN-13: 978-0789747419

The Tao of Twitter: Changing Your Life and Business 140 Characters at a Time by Mark Schaefer ISBN-13: 978-0071802192

Stand Out Social Marketing: How to Rise Above the Noise, Differentiate Your Brand, and Build an Outstanding Online Presence by Mike Lewis ISBN-13: 978-0071794961

A useful background resource is a free e-book by McHaney, R. W. (2012), 'Web 2.0 and Social Media for Business', [available from http://bookboon.com/en/web-2-0-and-social-media-for-business-ebook]

A reading list is available from MyWeb or the instructor.

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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 DIGIB201-18B (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 200 hours in total.
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Linkages to Other Papers

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Note any linkages to other papers where the linkage is of importance.
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Restricted papers: MSYS353, MSYS453 and DIGIB301

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