Social Media Analysis 10-802 in Spring 2011

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Announcements

  • 2/14. Second draft of your project proposals is Tuesday 2/15. - please link to them from here
  • 2/9. I've added a few more papers to the wiki, as suggestions for presentations. Some of these are under the first few lectures on sentiment.
  • 2/1. First draft of your project proposals are due - please link to them from here
  • 2/1. I added a list of tips for writing wiki pages for this course.
  • 1/19. My office hours will be Thursday afternoons, 1-2pm, starting on 1/28. This week I will have office hours on Friday, 1/21 from 4-5pm.
  • 1/17. To get a wiki account (which you'll need for the first assignment), just send an email message to Katie Rivard (krivard@andrew.cmu.edu) asking for one. Please let her know your andrew id - we'll use that as your username.
  • 1/5. This syllabus is now a reasonably close approximation to what the course is about.
  • 12/28. This syllabus is under construction.

Instructor and Venue

Clarification/announcement: This will be a regular 12-credit course (despite the some listings of it as a 6-credit course).

Description

The most actively growing part of the web is "social media" - e.g.. wikis, blogs, bboards, and collaboratively-developed community sites like Flikr and YouTube. This course will review selected papers from the recent research literature that address the problem of analyzing and understanding social media. Topics that will be covered include:

  • Text analysis techniques for sentiment analysis, analysis of figurative language, authorship attribution, and inference of demographic information about authors (e.g., age or sex).
  • Community analysis techniques for detecting communities, predicting authority, assessing influence (e.g. in viral marketing), or detecting spam.
  • Visualization techniques for understanding the interactions within and between communities.
  • Learning techniques for modeling and predicting trends in social media, or predicting other properties of media (e.g., user-provided content tags.)

Students should have a machine learning course (e.g., 10-601 or similar) or consent of the instructor. Readings will be based on research papers. Grades will be based on class participation, paper presentations, and a project. More specifically, students will be expected to:

  • Prepare summaries of the papers discussed in class. Summaries will be posted on this wiki.
  • Present and summarize one or more "optional" papers from the syllabus (or some other mutually agreeable paper) to the class.
  • Do a course project in a group of 2-3 people. The end result of the project will be a written report, with format and length appropriate for a conference publication.

Syllabus and Readings

Older syllabi:

The first half of the course, roughly, will be presentations of background material. Luckily there are some very good recent surveys on this.

The second half of the course will be presentations of recent research papers.

Other Resources

Grading

Grades are based on:

  • The paper presentation
  • The project (writeup and presentation).
  • Class participation.

I use some discretion in assigning grades but my guidelines for grading are announced in the overview talk.

Course Project

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