Social Computing
Course code
old course code
Course title in Estonian
Course title in English
Social Computing
ECTS credits
Assessment form
lecturer of 2021/2022 Spring semester
lecturer not assigned
lecturer of 2022/2023 Autumn semester
lecturer not assigned
Course aims
To give an overview of web2.0 tools and their design principles, the way they are used for social computation, and the way they are applied in the web and in the enterprise.
Brief description of the course
(1) Web 2.0 tools and social interactions they support: Review several Web 2.0 tools (e.g. wikis, weblogs, social tagging) and derive general principles of social interaction they support (e.g. emergence). Students do reading and analyze different examples of Web2.0 tools in smaller groups.

(2) Social Computation: Students do readings of different ways of how to employ social computation (e.g. collaborative filtering, online auctions, prediction markets, reputation systems, social choice, verification games). Performing one data analysis project in which they apply social computation principles to a particular problem with a particular dataset.

(3) Application in the web and in the enterprise: Students read and report on case studies about the application of web2.0 and social computation in the web and in enterprise settings.
Learning outcomes in the course
Upon completing the course the student:
- knows different types of tools and functionalities that support social interaction and understand general principles that govern their design;
- knows different ways of how these tools and functionalities allow for social computation (e.g. making intelligent recommendations, judgements or inferences);
- is able to apply some social computing mechanisms in a small dataset in a limited context;
- knows the benefits and potential risks involved in the application of social computation;
- understands and be able to apply these tools and principles in an enterprise setting in knowledge management or marketing.
Tobias Ley, PhD
Additional information
Students must participate in 80% of the class sessions.
Students must complete a short 2-3 page case analysis report, present it in class and comment other students’ work
Students must complete a data analysis project report and present it in-class
Students must participate in the in-class individual and group exercises, publish results of the exercises after class in their blog and comment others’ assignments