Social Media Analysis 10-802 in Fall 2012

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Announcements

  • The first class is Tuesday 9/11.
  • William's office hours will be 1-2pm Friday (except when otherwise noted).
    • William will be out of town Friday 9/21.

Instructor and Venue

  • Instructor: William Cohen, Machine Learning Dept and LTI
  • Course secretary: Sharon Cavlovich, sharonw+@cs.cmu.edu, 412-268-5196
  • When/where: T/R 10:30-11:50, 4303 GHC, starting after the IC has finished. The first class will be Tuesday 9/11.
    • Note: the room has been moved!
  • Course Number: MLD 10-802, cross-listed as LTI 11-772
  • Prerequisites: a machine learning course (e.g., 10-701 or 10-601) or consent of the instructor.
  • TAs: Bhavana Dalvi and Aasish Pappu
  • Syllabus: To best posted, but will approximately follow the syllabus I used last time
  • Office hours:
    • William: 1-2pm Fri
    • Aasish: 2-3pm Thu
    • Bhavana: 12-1pm Tue [location : GHC 5th floor lounge (LTI kitchen)]
  • Course mailing list for announcements: 10802-announce@cs.cmu.edu

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.

Sample Projects

Here are some projects from previous years, so you can get some ideas of the scope.

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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.

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