Module SOCI44115: Computational Social Science

Department: Sociology

SOCI44115: Computational Social Science

Prerequisites

• None

Corequisites

• None

Excluded Combination of Modules

• None

Aims

• Across the social sciences the methodological landscape has significantly changed to include a new repertoire of methodologies and methods grouped under the general heading of computational social science. These methodologies are part of the ‘complexity turn’ in the social sciences and variously sit within the complexity sciences, including such fields as data mining, digital social science, big data, systems modelling and visual complexity. The list of methods is rather significant, ranging from machine learning and complex network analysis to simulation and visual and textual analysis. What is key is that these methods extend and blur the boundaries of conventional statistics and qualitative inquiry, primarily through a focus on cases and their configurational complexity. The purpose of this module is to introduce students to computational social science, including a working knowledge of several of the most widely used methods.

Content

• Historical overview of the development of the complexity sciences - More specifically a review of the ‘complexity turn’ in the social sciences
• Survey of the field of computational social science
• Examination of the core links between computational social science and conventional statistics and qualitative inquiry
• Case-based complexity
• Classification and clustering
• Machine learning and data forecasting
• Dynamical modelling and simulation
• Complex network analysis

Learning Outcomes

• By the end of this module students will be able to:
• understand the history of the complexity sciences and the complexity turn in the social sciences
• understand key concepts in computational social science
• apply these ideas to social inquiry, specifically the student’s primary area of study
• link computational social science to other areas of inquiry, including the qualitative, historical, and statistical.

• By the end of this module students will be able to:
• have a general facility with the field’s key concepts and methodologies and methods
• have a basic understanding of the mathematics upon which these methods are based
• demonstrate a working knowledge of how to use some of the key software packages for running the methods learned in this class.

• deal with highly complex methodological issues and communicate conclusions to specialist and non-specialist audiences
• demonstrate a high degree of self-direction in using computational social science to engage in social inquiry, particularly related to the student’s primary area of research
• work autonomously in planning and implementing a computational social science method.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• Lectures: allow staff to introduce designated topic areas in a systematic manner. At MA level, students are encouraged to engage with lectures more proactively than is normally the case at undergraduate level - to this end, lectures act both as a 'point of departure' for knowledge accumulation, but also as a means of encouraging students to engage either with new ideas, or with familiar ideas buy at a more advanced level of debate.
• Seminars: enable students to explore and evaluate some of the key methods discussed in the module. Students will work in teams of two for the seminars.
• Directed Reading: module study guides provide students with information about core and further reading. Students are expected to read for seminar and explore the methods prior to the class. 'Directed reading' relates to books and other texts relevant to a particular seminar topic,
• Independent Reading: provides students with the opportunities to read widely, particularly in preparation for formative and summative. Independent reading enables students to draw on debates within scholarly journals and research monographs, in ways that enhance a critical understanding and engagement with key issues in computational social science.
• Summative work - summative essays test students' understanding of the major issues discussed in the module and to apply one of these methods to their topic of study. Students will complete a written assignment, involving a mock-up research project, using one of the methods learned in class, in application to a topic in which they are interested.
• Formative work - The optional formative essay provides an opportunity for students to receive feedback on the methods and methodologies and core concepts of computational social science prior to completing their summative work.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

An optional formative essay. Students will provide an overview of their strategy for completing their summative. Students can write a 500-word essay, do a pitch-to-peers during class or do a poster presentation. Students will receive feedback either in writing (for the essay) or verbally during class discussion or in person with the module convener.

■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University