COMM 106D: Data and Culture – Winter 2022 (UCSD Communication)
Instructor: Prof. Stuart Geiger ([email protected])
Time: MW 9-9:50am, F asynchronous and online (blended mode)
Course summary: Developments in artificial intelligence are being combined with unprecedented levels of personal data collection, which are used to make inferences about who we are, what we are interested in, and where we belong. In response, this course takes a cultural lens to issues around data and AI. What are the practices and politics of quantifying humans and society? How do technologies like personalized microtargeting and machine learning actually work? How are classic and contemporary culture industries (film, TV, journalism, video games, etc.) are using data and AI in their work? What are the implications of giving us the search results that will keep our eyeballs on the screen the longest? The issues that arise in representing culture through analyses of data date back to the first censuses in ancient times, but have taken a turn with new methods and data. What do these approaches capture and what do they miss?
Relationship to other COMM courses: This course may have a some amount of overlap with related courses in the department, including COMM 106E (Data, Science, and Society, which I’ll teach in S22), COMM 106I (Internet Industries), and COMM 162 (Culture Industries). However, it is designed to take a different perspective and cover a different set of topics. We will largely *not* be focusing on topics that are more central to these other courses, such as the role of data and AI in sectors like criminal justice, surveillance, banking, hiring, admissions, welfare benefits, or science — which will be the focus on 106E in S22.
Scheduling and hybrid format This class is officially scheduled for MWF 9-9:50am. Monday and Wednesday will be syncronous (Zoom or in-person depending on university administrative decisions). The Friday “hour” will be an asynchronous online participation component, which must be completed at a time of your choice between the end of class on Wednesday and 11:59pm on Saturday.
Prerequisites: This is an intermediate elective, so only COMM 10 as a prereq or a co-req. This means you can enroll in this class at the same time as taking COMM 10. You must submit an EASY request after enrolling in COMM 10. Seehttps://communication.ucsd.edu/undergrad/academic-overview/faqs.html#I-want-to-enroll-in-COMM-10-and. No other prereqs are required. We will learn some aspects about how data science and AI works, but this course assumes no prior knowledge of computing.
15% Mon/Wed in-person participation (you can miss up to 3 classes unexcused; 4 unexcused absences will be penalized 1 letter grade)
15% Friday virtual participation
20% Take home essays (400-500 words, 3 assignments, lowest grade dropped):
- Write about a real-world system of quantification and its consequences
- Write about an algorithmic imaginary of a social networking site
- Write about the use of data/AI in a culture industry
20% Group midterm performance/skit/parody project, comprised of:
- 5% proposal
- 10% group grade, with points distributed by group members
- 5% statement of group work
30% Final, comprised of:
- 5% Final paper/project proposal
- 25% Final paper/project
Midterm: The midterm will be a group performance project, in which groups of 3-5 students will produce a 5-7 minute segment on a topic related to the class. You will have to propose the topic beforehand. You are free to choose the format, genre, and whether it is recorded or live.
Final: The final will be a final paper/project of your choice related to the class content. The default format for the final project is a 1750-2000 word paper, but students who have experience with filmmaking, creative writing, web design, data science, etc., can propose an alternative format drawing on their skills.
Note: All readings will be made freely-available, so do not purchase any readings for this class. Some links require you to be on campus or logged in to the UCSD VPN.
Week 1: What is Data? What is Culture?
Mon, Jan 3rd: Lecture. Intro to class content and syllabus. No readings before class.
Wed, Jan 5th: Zoom lecture and activity. Read Williams (1976, https://stuartgeiger.com/resources/williams-culture-keywords-t.pdf) and Striphas (2016, https://stuartgeiger.com/resources/striphas-culture-keywords.pdf) before class.
Fri, Jan 7th: Asynchronous participation. Read Hey’s “The Data, Information, Knowledge, Wisdom Chain” (https://www.jonohey.com/files/DIKW-chain-Hey-2004.pdf). Then follow the instructions on the “Getting to know you survey” on canvas between Wed 9:51am and Fri 11:59pm. This is not just taking a survey, it is also coming up with questions for your fellow students.
Week 2: Quantifying humans:
M 1/10: Read “Algorithms and their others: Algorithmic culture in context” by Paul Dourish (https://journals.sagepub.com/doi/10.1177/2053951716665128)
W 1/12: Read “Algorithmic psychometrics and the scalable subject” by Luke Stark (https://journals.sagepub.com/doi/full/10.1177/0306312718772094)
F (async) 1/14: Making our own personality quizzes
Due by Sunday 1/16 5:00pm: Write a 400-500 word essay about a real-world system of quantification. Discuss its intended use and the issues that arise.
Week 3: Quantifying society:
M 1/17: Read ‘Cities, People, and Language’ by James C. Scott, from_Seeing Like a State_.
W 1/19: Listen to Malcolm Gladwell’s “Puzzle Rush” and “The Tortoise and the Hare” (Season 4, Ep 1 and 2 of “Revisionist History”). About 40 minutes each with ads.
F (async) 1/21: Making our own rankings of the best places to live
Week 4: Recommender systems and personalized recommendation:
M 1/24: No reading, lecture on how recommender systems work
W 1/26: No reading, lecture part 2 and designing our own system
F (async) 1/28: Looking inside our personalized models
Due by Sunday 1/30 5:00pm: 200-250 word proposal from your group about your performance project related to a topic we have or will cover in this class. This should contain 3 different ideas about what you want to do, including the possible genre and the format it will take.
Week 5: The News Feed:
M 1/31: No reading, lecture
W 2/2: Read “The algorithmic imaginary: exploring the ordinary affects of Facebook algorithms” by Taini Bucher (https://www.tandfonline.com/doi/abs/10.1080/1369118X.2016.1154086)
F (async) 2/4: The Facebook Demetricator and designing alternative news feeds
Due by Sunday 5:00pm: 200-250 proposal about what you might write your final paper about. This should contain 3 different ideas, including the format it could take (especially if you want to do something other than a paper)
Week 6: Social media content moderation:
M 2/7: No reading, lecture on computational foundation of content moderation
W 2/9: Read from_Behind the Screen_by Sarah Roberts, Intro and Ch 2.
F (async) 2/11: Auditing the results of content moderation algorithms
Due by Sunday 2/13 5:00pm: 400-500 word essay about an algorithmic imaginary around a social networking site. It could either be your own imaginary from your personal experience, a story about something from friends/family (anonymize, get permission from them), or from popular culture. Discuss what the imaginary is and how the operation of social networking site supports and/or undermines this imaginary.
Week 7: Netflix:
M 2/14: Read “How Netflix Reverse-Engineered Hollywood” by Alexis Madrigal (https://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/)
W 2/16: Read “Catered to Your Future Self: Netflix’s Predictive Personalization and the Mathematization of Taste” by Neta Alexander, from_The Netflix Effect_
F (async) 2/18: Diary of your streaming activity
Due by Sunday 2/20 5:00pm: A draft script of your group’s performance
Week 8: Search Engines
M 2/21: Read Algorithms of Oppression: How Search Engines Reinforce Racism by Safiya Noble, “Ch 1: A Society, Searching” and “3. Searching for People and Communities”
W 2/23: Read “Algorithmically recognizable: Santorum’s Google problem, and Google’s Santorum problem” by Tarleton Gillespie
F 2/25: Auditing search engine results: do we all see the same things?
Due by Sunday 2/27 5:00pm: A full outline of your final paper, with a 250-500 word introduction.
Week 9: Journalism
M 2/28: Read “Automation in the Newsroom” by Celeste LeCompte (https://niemanreports.org/articles/automation-in-the-newsroom/)
W 3/2: Read “What APIs can do for news” by David Weinberger (https://niemanreports.org/articles/what-apis-can-do-for-news/)
F 3/4: Building our own newsbots
Due by Sunday 3/6 5:00pm: 400-500 word essay about the role of data and/or AI in a culture industry of your choice. Your arguments should be based in primary and secondary sources, and you should cite at least 4 different sources. If you write about an industry we already covered, you must find 2 additional sources.
Week 10: Group performances and final project prep