THE UNIVERSITY of EDINBURGH

DEGREE REGULATIONS & PROGRAMMES OF STUDY 2022/2023

Timetable information in the Course Catalogue may be subject to change.

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DRPS : Course Catalogue : Edinburgh Futures Institute : Edinburgh Futures Institute

Postgraduate Course: Evidence, Argument and Persuasion in a Digital Age (fusion online) (EFIE11081)

Course Outline
SchoolEdinburgh Futures Institute CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate)
Course typeOnline Distance Learning AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryHow do rumours spread on instant messengers? How can I be aware of what customers are saying online about my business? Can bots on social media shape public opinion on political issues? How can I win an argument in a world of memes? This 10-credit elective is designed to give students insights into the dynamics of online discussions, their participants, and the computational methods that are available for studying them.
Course description The course conveys theoretical background knowledge drawing from decades of research in a variety of social science disciplines and practical skills in current computational methods and software tools.

Prior to the intensive period, students learn relevant theoretical concepts and gain practical skills in using key tools using a combination of introductory videos, self-directed study, and set tasks that need to be completed and posted on an online discussion board. During the two-day intensive phase, students analyse data-driven case studies in groups, present their analysis and recommendations to peers and critically evaluate the analysis of others. They participate in guided discussions in which they draw from their own and each other's experiences to draw their own conclusions, informed by the knowledge they gained in the course. The post-intensive phase is dedicated to the production of the assessed coursework.

Syllabus:

- Welcome and introduction;
- Practical introduction to using tools and methods for computational social science, including social network analysis, data visualisation and statistical analysis (such as Gephi, Tableau, Jupyter Notebook / Noteable);
- Theories and concepts related to evidence, argumentation and persuasion, drawing on a wide range of disciplines including informatics, communication, marketing, philosophy and social psychology;
- Data-driven investigation of cases related to the course topic;
- Production of a multimedia document that may contain text, graphs, tables, video, audio and/or code on the course topic.

Edinburgh Futures Institute (EFI) - Online Fusion Course Delivery Information:

The Edinburgh Futures Institute will teach this course in a way that enables online and on-campus students to study together. This approach (our 'fusion' teaching model) offers students flexible and inclusive ways to study, and the ability to choose whether to be on-campus or online at the level of the individual course. It also opens up ways for diverse groups of students to study together regardless of geographical location. To enable this, the course will use technologies to record and live-stream student and staff participation during their teaching and learning activities. Students should note that their interactions may be recorded and live-streamed. There will, however, be options to control whether or not your video and audio are enabled.

As part of your course, you will need access to a personal computing device. Unless otherwise stated activities will be web browser based and as a minimum we recommend a device with a physical keyboard and screen that can access the internet.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesNone
Course Delivery Information
Academic year 2022/23, Available to all students (SV1) Quota:  10
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 5, Online Activities 10, Formative Assessment Hours 2, Other Study Hours 13, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 68 )
Additional Information (Learning and Teaching) Other Study: Scheduled Group-work Hours (hybrid online/on-campus) - 13
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Summative Assessment:

The course will be assessed by means of the following assessment component:

1) Computational Notebook (100%)

Each student will produce a computational notebook that may contain text, code and/or multimedia content (such as data visualisations, screencasts, video essays) in response to an open question, drawing on course concepts and methods. Data, graphs and tables may be used to support the argument, where appropriate. The maximum length for all textual content (excluding code) is 1500 words, the maximum length for all multimedia content combined is ten minutes.

The option will be provided to publish a selection of submissions to a public-facing EFI blog.
Feedback Feedback on the formative assessment may be provided in various formats, for example, to include written, oral, video, face-to-face, whole class, or individual. The course organiser will decide which format is most appropriate in relation to the nature of the assessment.

Feedback on both formative and summative in-course assessed work will be provided in time to be of use in subsequent assessments within the course.

Feedback on the summative assessment will be provided in written form via Learn, the University of Edinburgh's Virtual Learning Environment (VLE).

During the pre-intensive teaching phase, academic staff and peers will give feedback to each student's reflections on core readings and to their results for data-driven tasks on the online discussion board.

In the intensive block, staff and peers will provide immediate feedback on group presentations of case results.
Staff can provide informal feedback on ideas for summative assessment in drop-in Q&A sessions.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Critically understand the principal concepts of social data science and relevant theories as they apply to the study of evidence, argument and persuasion in a digital age.
  2. Apply a range of standard and specialised research instruments to the study of social media data.
  3. Apply critical analysis, evaluation and synthesis to issues that are informed by forefront developments in computational social science.
  4. Develop original and creative responses to problems and issues facing professional users of social and digital media in the absence of complete or consistent information.
  5. Communicate complex ideas and results about social media data and recommended actions to peers, more senior colleagues and specialist.
Reading List
Indicative Reading List:

Essential:

Ferrara, E., Varol, O., Davis, C., Menczer, F., & Flammini, A. (2016). The rise of social bots. Communications of the ACM, 59(7), 96-104.

Gilbert, M. A. (2014). Arguing with people. Broadview Press.

Tan, C., Niculae, V., Danescu-Niculescu-Mizil, C., & Lee, L. (2016). Winning arguments: Interaction dynamics and persuasion strategies in good-faith online discussions. In Proceedings of the 25th International Conference on World Wide Web (pp. 613-624).

Recommended:

Bruns, A., Enli, G., Skogerbo, E., Larsson, A. O., & Christensen, C. (Eds.). (2015). The Routledge companion to social media and politics. Routledge.

Carnegie, D., & Cole, B. (2011). How to Win Friends and Influence People in the Digital Age. Simon & Schuster, 2011

Nagle, A. (2017). Kill all normies: Online culture wars from 4chan and Tumblr to Trump and the alt-right. John Hunt Publishing.

Ross, B., Pilz, L., Cabrera, B., Brachten, F., Neubaum, G., & Stieglitz, S. (2019). Are social bots a real threat? An agent-based model of the spiral of silence to analyse the impact of manipulative actors in social networks. European Journal of Information Systems, 28(4), 394-412.

Further:

Highfield, T. (2017). Social media and everyday politics. John Wiley & Sons.

Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. Yale University Press.
Additional Information
Graduate Attributes and Skills - Students will develop critical understanding of the role evidence, argument and persuasion play in communication on digital and social media (SCQF characteristic 1).

- Students will acquire skills in using a range of specialised techniques from the forefront of computational social science research that will enable them to make evidence-based judgements about online communication (SCQF characteristic 2).

- Students will gain the ability to critically review current thinking on the role of evidence, argument and persuasion for successful online communication and they will be able to identify, conceptualise and define new problems in this area (SCQF characteristic 3).

- Students will gain the skills to undertake critical evaluations of numerical and graphical data obtained from social media and communicate their findings using a wide range of ICT applications (SCQF characteristic 4).

- Students will gain autonomy and accountability by working with others on the course material, taking responsibility for their work and that of others and making informed judgements about professional issues relevant to evidence, argument and persuasion in the digital age (SCQF characteristic 5).
KeywordsEvidence,Argumentation,Persuasion,Digital Media,Social Data Science,Social Network Analysis
Contacts
Course organiserDr Bjorn Ross
Tel: (0131 6)50 3128
Email: b.ross@ed.ac.uk
Course secretary
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