Postgraduate Course: Managing Digital Influence (PGSP11391)
Course Outline
| School | School of Social and Political Science |
College | College of Humanities and Social Science |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | One of the most impactful effects of easier access to a larger proportion of data on an increasing number of phenomena is the use of rankings to assess all aspects of the performance of products and organizations based on customer feedback. This 10 credits course provides students with skills to (1) develop a comprehensive understanding of the making of organizational reputation indices; (2) compare different methods to collect data on digital influence; (3) capture the effects of rankings on organizations; (4) manage reputation risk in the light of new social media-based ranking systems. Our analysis will start from media ranking and progressively extend to automated ranking systems. The course also offers a tutorial on Using Gephi as a tool for Measuring online Influence. |
| Course description |
Week 1 Media Rankings
In the first week, we will provide an overview of popular media rankings and discuss aspects of their making.
Week 2 Assessing Digital Influence
We will then describe the different ways of assessing digital influence. One is social, interaction-based model whilst the other relies heavily on the use of social media data. The model of assessing influence based on personal familiarity and informal networking channels is considered in relation with important recent developments based on making use of data analytics tools (like Klout, PeerIndex, Kred etc¿). We will ask: Is the new model measuring influence through counting numbers of ¿followers¿ and re-tweets going to replace more traditional ¿qualitative¿ methods in assessing influence? Are these two wholly differentiated processes or there is an amount of cross-over within these practices?
Week 3 Using Gephi to Measure Digital Influence
The course offers a tutorial on Using Gephi as a tool for Measuring online Influence. This gives students an opportunity to engage with social network modelling in order to explain and visualise influence in specific networks. Using third party interfaces, Twitter data will be filtered and imported to Gephi in order to measure and visualise influence on 1) specific networks e.g. ¿who are your most influential followers?¿and 2) specific keywords ¿who are the most influential user on a specific keyword?¿. To answer these questions different methods will be used and illustrated to give students an overview of existing possibilities.
Week 4 New ranking methods based on social media data: are they successful?
We will then extend our discussion to include the ¿entrants¿ in the ranking ecosystem. Internet and social media technologies have lowered the barrier to access the ranking market. Plurality of rankers may in theory make a difference, and the power of existing ranker can be weakened. We will host a guest lecture by a member of the Institute of Industry Analyst Relations (IIAR) to speak about whether IT organisations have changed their attitude towards rankers. The guest lecturer will discuss hands on experience of the effect on rankings in the IT sector, with particular reference to consultancy service and whether new methods of analysis have affected the market of supplementary knowledge about on-line influence measures.
Virtual Classroom : Guest lecture from Member of IIAR
Week 5 What rankings do to organizations
We will discuss what rankings do to organizations, in terms of how external audience react to rakings and the influence of prior rakings on surveys that determine future ranks, the use of rankings to make funding decisions and how activities within organizations conform to ranking criteria. We will present studies that demonstrate how on-line rankings can have complex effects on organizational behavior, creating processes of ¿reactivity¿ whereby, in an effort to improve a position, organizations begin to conform to these measures.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
Course Delivery Information
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| Academic year 2015/16, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 1 |
| Course Start Date |
21/09/2015 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
98 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
Knowledge and Understanding of Digital Research
- critical understanding the range of theories, principles and concepts available to assess evidence deriving from monitoring digitally derived internet data;
- critical awareness strengths and limitations in comparison to other ways of apprehending customer needs;
Applied Knowledge and Understanding of Digital Research
- make best use of the results of digital data analytics for service design, marketing and institutional reputation management;
- - identify, access and commission on-line data analytics tools and services appropriate to their needs;
Cognitive Skills in Digital Research
- evaluate the benefits and limitations of digital data for organizational decision-making;
- understand when and how to procure social media data analytics services and how to combine them with existing knowledge practice.
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Reading List
Downes, D. (2000). Does BusinessWeek ranking matter? The MBA Newsletter, 8(9), 5¿10.
Espeland, W., Sauder, M. (2009). Rating the Rankers. Contexts, Vol. 8, No. 2, pp. 16¿21.
Espeland, W. N., & Sauder, M. (2007). Rankings and Reactivity: How Public Measures Recreate Social Worlds1. American Journal of Sociology, 113(1), 1-40.
Fombrun, C., Shanley, M. (1990) What's in a Name? Reputation Building and Corporate Strategy, The Academy of Management Journal, 33 (2), pp. 233-258.
Reingold, J., & Habal, H. (1998). How we kept the data unsullied. Business Week, 19(October), 94.
Hillis, K., Petit, M., Jarrett, K. (2013). Google and the Culture of Search. New York, Routledge.
Schultz, M., Mouritsen, J., & Grabielsen, G. (2001). Sticky reputation: Analyzing a ranking system. Corporate Reputation Review, 22, 24¿41.
Welch, I. (2002). The 2000 business week rankings of business schools: Why they are both harmful and wrong. Available at: «http://welch.som.yale.edu/academics/bweek.html». Accessed 7.11.2006.
Yee, C. (2004). Ranking methods under fire. University Wire, 4(September), 1.
Weblinks:
Interview with Espeland and Sauder on Context Podcast (from minute 5): http://thesocietypages.org/officehours/2009/07/16/ranking-colleges-and-supernatural-beliefs/
Webinar on The Impact of Social on the Analyst Industry: A Roundtable w/ Jonny Bentwood, Barbara French, Carter Lusher, and Jeremiah Owyang: http://vimeo.com/13520800
Cornell University study on how Amazon manufacture book reviews:
http://www.freelunch.me
University of Oxford research on online rankings:
http://www.howsmyfeedback.org/
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Gian Campagnolo
Tel: (0131 6)51 4273
Email: g.campagnolo@ed.ac.uk |
Course secretary | Miss Jade Birkin
Tel: (0131 6)51 1659
Email: Jade.Birkin@ed.ac.uk |
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