Course

Digital Culture and Surveillance Society

Faculty
Humanities and Social Sciences
Department
Sociology
Course code
SOCI 3385
Credits
3.00
Semester length
15 Weeks
Max class size
35
Method(s) of instruction
Lecture
Seminar
Course designation
None
Industry designation
None
Typically offered
To be determined

Overview

Course description
This course involves an advanced examination of digital culture and social surveillance, providing insights into the evolving relationship between society and technology in an increasingly digitalized world. Digital sociology explores new vectors of power and inequality that arise due to our increasing reliance on technologies that introduce surveillance into many aspects of daily life. Course materials explore scholarship in digital sociology and surveillance culture, challenging the perspective that digital life, algorithms, and surveillance are neutral practices. Foundational sociological themes related to power and marginalization including race, gender, sexuality, disability, and class will be central to the analysis. Course content will explore how big data processes in society contribute to surveillance culture by examining topics such as: risks to data accuracy, privacy, and security; algorithmic bias and discrimination; prosumption and surveillance capitalism; and the use of AI in digital surveillance. Analytical assessment of these issues will enable students to develop critical data literacy and consider the potential for data justice in organizations and society.
Course content
  1. Introduction: What is digital sociology? Central concepts and concerns
  2. Sociological theory in digital society
  3. What is digital culture? Digital self, digital relationships and digital communities
  4. AI as a sociotechnical system
  5. Defining the culture of surveillance
  6. Sociological theories of surveillance: Foucault and beyond
  7. Developing critical data literacy
  8. What is big data and why does it matter?
  9. AI and digital surveillance
  10. Prosumption and consumer surveillance
  11. Social inequality and the surveillance society: Bias in big data and algorithms
  12. Data activism and the potential for data justice
Learning activities

The course will employ a number of instructional methods to accomplish its objectives, including some or all of the following:

  • small and large group discussions
  • audio-visual materials
  • seminar presentations
  • lectures (including guest lectures)
Means of assessment

Evaluation will be carried out in accordance with the Douglas College Evaluation Policy. Specific evaluation criteria will be provided by the instructor at the beginning of the semester.

Instructors may use a student’s record of attendance and/or level of active participation in the course as part of the student’s graded performance. Where this occurs, expectations and grade calculations regarding class attendance and participation will be clearly defined in the Instructor Course Outline.

Evaluation will be based on some or all of the following:

  • Participating in class discussion
  • Essays
  • Oral presentations (individual and/or group)
  • Written exams

A sample grade breakdown for this course might be as follows:

Participation - 10%

Midterm Exam - 15%

Research Paper - 30%

Seminar Presentation - 20%

Critical Media Analysis - 10%

Final Exam - 15%

Students may conduct research with human participants as part of their coursework in this class. Instructors for the course are responsible for ensuring that student research projects comply with College policies on ethical conduct for research involving humans.

This is a letter-graded course. 

Learning outcomes

By the end of the course, successful students should be able to:

  1. Locate the emergence of digital sociology and surveillance studies as fields of inquiry within sociology
  2. Interpret key sociological theories within contemporary sociological scholarship on digital culture
  3. Explain foundational and contemporary issues in the sociological study of digital culture
  4. Interpret key sociological theories within contemporary sociological scholarship on surveillance, such as governmentality and risk society
  5. Critically evaluate key areas of scholarship, such as late-modern subjectivity, advanced capitalism, neoliberalism, globalization
  6. Identify and critique forms of surveillance that impact the lives of individuals and the structure of contemporary digitalized societies
  7. Apply critical data literacy in the exploration of course themes
  8. Explain the need for data justice in an increasingly digitalized society
Textbook materials

Below is a sample text that could be used in this course:

Orton-Johnson, Kate (2024). Digital Culture and Society. London: Sage.

Below is a list of relevant academic articles and books that could be used to design a course reader.

Ball, Kirstie. Haggerty, Kevin, and David Lyon (eds) (2014). Routledge Handbook of Surveillance Studies. London: Routledge.

Beck, Ulrich. (1992). Risk Society: Towards a New Modernity. London: Sage.

Burchell, Graham, Colin Gordon, and Peter Miller (eds.). (1991). The Foucault Effect: Studies in Governmentality. University of Chicago Press.

De Mauro, Andrea, Greco, Marco, and Michele Grimaldi (2015). What is Big Data? A Consensual Definition and a Review of Key Research Topics. International Conference on Integrated Information (IC-ININFO 2014) AIP Conf. Proc. 1644: 97-104.

Dencik, Lina, Hintz, Arne, Redden, Joanna and Emiliano Treré (2022). Data Justice. London: Sage.

Dubrofsky, Rachel and Shoshana Amielle Magnet. (2015). Feminist Surveillance Studies. Duke University Press.

Gilliom, John and Torin Monahan (2013). SuperVision. University of Chicago Press.

Joyce and Cruz (2024). A Sociology of Artificial Intelligence: Inequalities, Power, and Data Justice. Socius: Sociological Research for a Dynamic World 10: 1–6.

Lupton, Deborah (2015). Digital Sociology. New York: Routledge.

Lyon, David (2007). Surveillance Studies: An Overview. Cambridge: Polity Press.

Lyon, David (2018). The Culture of Surveillance. Cambridge: Polity Press.

Noble, Safiya Umoja (2017). Algorithms of Oppression: How Search Engines Reinforce Racism. New York University Press.

Pangrazio, Luci and Neil Selwyn (2023). Critical Data Literacies: Rethinking Data and Everyday Life. Cambridge, Mass: The MIT Press.

Scwartz, Ori (2021. Sociological Theory for a Digital Society, Cambridge: Polity Press.

Selwyn, Neil (2019). What is Digital Sociology? Cambridge: Polity Press.

Taylor, Linnet (2017). What is data justice? The case for connecting digital rights and freedoms globally. Big Data & Society, July–December 2017: 1–14.

Requisites

Course Guidelines

Course Guidelines for previous years are viewable by selecting the version desired. If you took this course and do not see a listing for the starting semester / year of the course, consider the previous version as the applicable version.

(Current)

Course Transfers to Other Institutions

Below are current transfer agreements from Douglas College to other institutions for the current course guidelines only. For a full list of transfer details and archived courses, please see the BC Transfer Guide.

Institution Transfer details for SOCI 3385
Athabasca University (AU) AU SOCI 3XX (3)
Camosun College (CAMO) CAMO HUM 2XX (3)
College of the Rockies (COTR) COTR SOCI 2XX (3)
Columbia College (COLU) COLU SOCI 2nd (3)
Emily Carr University of Art & Design (EC) EC SOCS 300 lev (3)
Kwantlen Polytechnic University (KPU) KPU SOCI 3XXX (3)
Okanagan College (OC) OC SOCI 3XX (3)
Simon Fraser University (SFU) SFU SA 304 (3)
Trinity Western University (TWU) TWU SOCI 3XX (3)
University Canada West (UCW) UCW SOCI 3XX (3)
University of British Columbia - Okanagan (UBCO) UBCO SOCI_O 2nd (3)
University of British Columbia - Vancouver (UBCV) UBCV SOCI_V 2nd (3)
University of Northern BC (UNBC) UNBC SOSC 3XX (3)
University of the Fraser Valley (UFV) UFV SOC 3XX (3)

Course Offerings

There are no course offerings this semester.