Thursdays 3:00-4:45 // SSMS 1310 // Winter 2017
Gateway Seminar for Info Tech and Society Graduate Certificate (CITS)
Prof. Amr El Abbadi (Computer Science) and Prof. Laila Shereen Sakr (Film and Media)
Office hours: (Prof. Sakr: Tuesdays 2:30-4:00, 2020 SSMS// and Prof. El Abbadi)
Research in all fields is increasingly concerned with, and inseparable from, the technologies and cultures producing information, algorithms and large-scale data sets. Whether studying, designing or using algorithms, researchers need to understand how their questions intersect with the logics of automation and scale underpinning networked, computational platforms. In this graduate seminar, students will take a hybrid approach to analyzing the various procedural algorithms, their relationship with their structured data (for example, tweets), and their impact on the contemporary, virtual body politic. Students will also get hands-on experience with tools, platforms, and activities; discuss recent controversies and examine how researchers have responded. This graduate seminar is open to interdisciplinary students from Social Sciences, Arts and Humanities, Engineering and Computer Science. Students will receive 2-units, Pass/No Pass credit and will be expected to conduct weekly presentations, write and comment on the class blog, and work in teams to develop analytics and measure trends using social media data.
- To demystify the algorithm; and in place, offer a theory for understanding it through humanistic inquiry into the logic of cultural objects, narratives, or processes for critical making.
- Test our theories through practice — learn a practice that questions the logic of the algorithm and its data.
Though not an exhaustive list, these questions guide our inquiry:
- History of the algorithm predates computation: The term ‘algorithm’ predates the digital computer by over a thousand years, with an etymology traceable to the Islamic scholar al-Khwārizmī. Are contemporary algorithms a necessarily computational phenomenon? How can we understand the explosion of discourse about algorithms in popular culture in the last decade?
- Algorithms as more than computation: What does it mean to study algorithms as myth, narrative, ideology, discourse, or power? In what ways can these approaches contribute back to concepts and questions within computer science, data science, and big data initiatives?
- Algorithms as specifically computational: What kinds of applications and activities are now possible given certain developments in computational infrastructure and theories of computation, such as big data, deep neural networks, distributed computing, or ‘microwork’? What are the social and theoretical implications of these developments?
- Algorithmic bias: Training data encodes biases; data collection feedback loops (find an area with crime, send police there, arrest more people, more crime stats); subpopulation have different observed trends, minority populations necessarily represent smaller portion of data. How can we measure algorithmic bias, if people are biased at any rate?
- Living with algorithms, quantifying social behavior: Algorithms pervade daily life and we experience their reach and impact almost anywhere, not just while working at a computer. How can we better understand how far-flung domains are being reshaped by algorithms? What are the consequences of big data and the quantified self in the everyday and in civic life?
- Weekly blogs and discussion: Each week a group of students will lead the readings discussion, suggest other readings, post to the blog, and respond to the blog posts. These are graduate-level, thoughtfully written posts.
- Final project: Students will work in interdisciplinary teams of two to develop and test at least one research question. Each team will submit a 500-word description of what they will build for the final and what questions/problems is the app trying to address by midterm. And for the final assignment, the teams will submit code that attempts to do something/ a software app, ideally in response to the research questions. The midterm and finals must be submitted through a blog post on the class website.
Sentiment analysis app
Network Visualization app
R-Shief is a Media System for Research: “We are media makers, researchers, and software developers who create open tools and visualizations, generate and host open data, and support open learning scholarship related to social media, global media activism”
Each team will use R-Shief to build its own “parameters” for collecting Twitter data, and then use it to export, or use in a new app, or visualize the live data. Login demo first day of class.
LOGIN HERE to start your own collections.
WEEK I (Jan 12) – Introductions, Class Mechanics
WEEK II (Jan 19) – The Study of Algorithms and Culture
El Abbadi, Amr (2017) What Are Algorithms?.
Hamish Robertson and Joanne Travaglia (2015) “Big data problems we face today can be traced to the social ordering practices of the 19th century.”
Manovich, Lev (2016) “The Science of Culture?” Cultural Analytics, Social Computing, and the Digital Humanities.”
Striphas, Ted (August-October 2015) “Algorithmic Culture,” European Journal of Cultural Studies 18: 4-5, 395–412.
WEEK III (Jan 26) – (In)computability: Approximation, randomization, completeness/reductions
MacCormick (2012) “What is Computable?” 9 Algorithms that Changed the Future: the Ingenious Ideas that Drive Today’s Computers. Princeton University Press: Princeton.
Kugler, Logan (June 2016) “What Happens When Big Data Blunders?” Communications of the ACM, Vol. 59 No. 6, Pages 15-16. 10.1145/2911975
WEEK IV (Feb 02) – Pattern Recognition & Civic Imagination
MacCormick, John (2012) “Pattern Recognition: Learning from Experience” 9 Algorithms that Changed the Future: the Ingenious Ideas that Drive Today’s Computers. Princeton University Press: Princeton.
After a 20-minute discussion on trending, we will collectively attend Professor Henry Jenkins lecture on “Remixing the Civic Imagination” in HSSB.
Henry Jenkins (Communication, Journalism, Cinematic Arts and Education, USC)
Thursday, February 2, 2017 / 4:00 PM
McCune Conference Room, 6020 HSSB
WEEK V (Feb 09) – Search (Abstracts are due)!!
MacCormick, John (2012) “Search Engine Indexing: Finding Needles in the World’s Biggest Haystack”. 9 Algorithms that Changed the Future: the Ingenious Ideas that Drive Today’s Computers. Princeton University Press: Princeton.
Solon, Olivia and Sam Levin (16 Dec 2016) “How Google’s search algorithm spreads false information with a rightwing bias,” The Guardian.
Cadwalladr, Carole (04 Dec 2016) “Google, democracy and the truth about internet search,” The Guardian.
WEEK VI (Feb 16) – Page Rank
MacCormick, John (2012) “PageRank: The Technology That Launched Google” 9 Algorithms that Changed the Future: the Ingenious Ideas that Drive Today’s Computers. Princeton University Press: Princeton.
Ghose, Anindya, Ipeirotis, P., Li, B. (18 August 2013) “Examining the Impact of Ranking on Consumer Behavior and Search Engine Revenue” Management Science.
WEEK VII (Feb 23) – Trending
Gillespie (2016) “#TrendingisTrending: When Algorithms Become Culture.”
Facebook (2015) “Trending Review Guidelines.”
WEEK VIII (Mar 02) – Social Media
Gillespie, Tarleton (May 18, 2016) “Algorithms, clickworkers, and the befuddled fury around Facebook Trends.”
Gillespie, Tarleton (May 9, 2016)“Facebook Trending: It’s made of people!! (but we should have already known that).”
Isaac, Mike (12 Nov 2016) “Facebook, in Cross Hairs After Election, Is Said to Question Its Influence” The New York Times.
WEEK IX (Mar 09) – Group Presentations
WEEK X (Mar 16) – Group Presentations (Projects due)!!!