Show The Graduate Center Menu
 
 

Social & Cultural Computing

Instructor: Professor Lev Manovich


Course description:

“The next big idea in language, history and the arts? Data.”
New York Times, November 16, 2010.
 

The joint availability of massive social and cultural data sets (including social media and digitized cultural artifacts) make possible fundamentally new paradigms for the study of social and cultural activities and histories.  While the recently emerged field of social computing started to explore some of these possibilities, we are only at the very beginning.
 
We will cover the following practical topics: 

  • basic concepts and methods of data analysis (using R);

  • basic visualization techniques (using in R / Mondrian / Tableu);

  • use of visualization for explorative data analysis;

  • elements of graphic  design as they relate to visualization and project web site design;

  • strategies for presenting projects online;

  • how to write effective project descriptions for the web presentation;

  • promoting projects through social media and getting media coverage;
     

(Image analysis will be used to illustrate basic concepts of exploratory data analysis.)
 
We will also examine both papers from computational social science science and data analysis/visualization projects by designers and artists.


Projects:

Note that is not a standard computer science course on data mining or machine learning. Instead, we will learn some basic techniques for analysis and visualization, and focus  on applying them to create interesting and professionally presented online projects.
 
The students will work together in groups  to conceptualize and complete computational projects which use big social/cultural data.
 
Each team will identify new interesting and important questions which have not yet been asked, prepare data sets sets, analyze them, create visualizations, and professional looking project web site about the project.  (The project can also result in an interactive app, or interactive artwork).
 
The students will have a choice of using any of the cultural data sets already prepared  by the instructor's lab - or gather new data sets from the web or other sources.

The available data sets include: one million Manga pages with metadata; One million images extracted from the books at British Library; hundreds of thousands of Instagram photos with metadata.
 
The students can work on data sets of any type (images, video, text, network data, spatial data;  structured or non-structured data).
 
They can also employ any programming language and libraries. (Class lectures on basic data analysis and visualization techniques will use mostly R).


The prerequisites:

The prerequisites for this class are:
 
1) curiosity and interest in using large data and computational methods to ask questions relevant for society;
 
2) Background in at least one of  the following areaa: classical statistics, multivariable data analysis, big data, machine learning, information visualization, digital image processing, computer vision, text analytics; network analysis.
 
3) Good experience in at least on contemporary programming/ scripting language.


Social and cultural data analysis / visualization - examples:

To get a better idea of the existing research and how projects are presented online, take a look at some examples of diverse  projects / papers which analyze and/or  visualize large social/cultural datasets:

http://www.niemanlab.org/2013/12/interrogating-the-network-the-year-in-social-media-research/
phototrails.net
livehoods.org  
http://arxiv.org/pdf/1308.3657v1.pdf
http://www.wired.com/wiredscience/2013/12/the-best-maps-of-2013/?cid=co16368954
http://www.theatlanticcities.com/technology/2013/07/most-sophisticated-flickr-maps-weve-ever-seen/6186/
http://moritz.stefaner.eu/projects/emoto/
http://notes.variogr.am/post/53245962722/is-your-movie-and-music-preference-related
Cinemetrics

Also please check the vision and the projects from Dr. Manovich’s lab:
 
http://lab.softwarestudies.com/2008/09/cultural-analytics.html
http://lab.softwarestudies.com/p/research_14.html


Schedule:

1: class introduction

2: data types, data organization, features, feature extraction, multi-dimensional feature space

3: exploratory data analysis -  visualizing one and two and multiple variables

4: exploratory data analysis -  visualizing multiple variables / spatial data / network data

5: elements of computational data analysis: distances, distance matrix, MDS, PCA

6: elements of computational data analysis: cluster analysis, into to classification

7: social computing: selected papers and projects

8: cultural computing: selected papers and projects

9: geo-spatial data: selected papers and projects

10: creative visualization of cultural and social  data:  selected projects / visualizing space and time

11: elements of graphic design for visualization

12: strategies for organizing online projects

13: capturing data about the world and humans in history  / politics of big data in society

14: final projects presentation