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Social & Cultural Computing

 
 

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);

  • use of visualization for explorative data analysis;;

  • 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.
 

Learning Goals:

1)  general understanding of current research directions in social and cultural computing;
 
2)  identifying not yet explored possibilities in working with social media data;
 
3) learning how to prepare data for analysis;
 
4) learning basic techniques for data exploration;
 
5)  becoming proficient with data visualization techniques;
 
6)  understanding of the structure and organization of web projects that present results of social and computing projects, or visualizations of cultural data;
 
7)  learning how to clearly and effectively write project summaries for general audiences;
 
8)  learning how to contact members of the press / getting project publicity and promote projects using social media;
 

Assessment:

Students will complete 3 practical assignments which involve organizing data set, analysing them and creating effective visualizations. The goals of these assignments is to meet learning goals 3-5. Students will work in groups on final projects which address goals 6-8. They will be also responsible to completing and discussing readings and sample projects (goals 1-2).


The Prerequisites:

The prerequisites for this class are:

1) curiosity and interest in using large data and computational and visualization methods to ask questions relevant for society;

2) background in at least one of  the following areas: statistics, multivariable data analysis, machine learning, data visualization, digital image processing, text analytics, digital humanities.

3) experience with least one contemporary programming/scripting language, or with organizing and analysing data using professional software such as R or Matlab.
 
If you have done digital humanities projects, this can be substituted for (3) with instuctor’s permission.
 

Final 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 from NYC and other cities.
 
The students can work on data sets of any type (images, video, text, network data, spatial data;  structured or unstructured data).
 
They can also employ any programming language and libraries. (Class lectures on basic data analysis and visualization techniques will use mostly R).


Social and cultural data analysis and 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 research and the projects from Dr. Manovich’s lab:
 
http://selfiecity.net/
http://phototrails.net/
http://lab.softwarestudies.com/2008/09/cultural-analytics.html


Course Schedule:

1: class introduction

2: capturing data about the world and humans - technologies and examples of art projects

3: social computing: selected papers and projects

4: cultural computing: selected papers and projects

5: some basic data analysis concepts and techniques

6: presentation of student proposals for class projects

7: analyzing large spatial data: selected papers and projects 
 
8: politics of big data in society

9: presentations and discussions of pilot projects

10: creative visualization of cultural and social data

11: elements of graphic design for visualization and project presentation

12: working on final project

13: presentation and discussion of final projects

14: fine tuning final projects