Difference between revisions of "Social Media Analysis 10-802 in Spring 2011"

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* [http://www.cs.cornell.edu/home/llee/opinion-mining-sentiment-analysis-survey.html Opinion mining and sentiment analysis], by Bo Pang and Lillian Lee, in ''Foundations and Trends in Information Retrieval 2(1-2), pp. 1–135, 2008''.  Also available as a book or e-book.
 
* [http://www.cs.cornell.edu/home/llee/opinion-mining-sentiment-analysis-survey.html Opinion mining and sentiment analysis], by Bo Pang and Lillian Lee, in ''Foundations and Trends in Information Retrieval 2(1-2), pp. 1–135, 2008''.  Also available as a book or e-book.
* [http://www.cs.cornell.edu/home/kleinber/networks-book/ Networks, Crowds, and Markets], by David Easley and Jon Kleinberg.  This very readable text will be published, as a textbook for an undergrad class, sometime this yearFor now it's available free on-line.
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* [http://www.cs.cornell.edu/home/kleinber/networks-book/ Networks, Crowds, and Markets], by David Easley and Jon Kleinberg.  This very readable text has been recently published, as a textbook for an undergrad class.  PDF is still available free on-line.
 
* [http://arxiv.org/abs/0912.5410/ A survey of statistical network models], Goldenberg, Zheng, Fienberg, and Airoldi.  A soon-to-be-published survey article.
 
* [http://arxiv.org/abs/0912.5410/ A survey of statistical network models], Goldenberg, Zheng, Fienberg, and Airoldi.  A soon-to-be-published survey article.
  

Revision as of 11:12, 4 January 2011

Announcements

  • 12/28. This syllabus is under construction.

Instructor and Venue

  • Instructor: William Cohen, Machine Learning Dept and LTI
  • Course secretary: Sharon Cavlovich, sharonw+@cs.cmu.edu, 412-268-5196
  • When/where: Mon-Wed 10:30-11:50, GHC 4303
  • 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.
  • TA: there is no TA for this course
  • Syllabus: Syllabus for Analysis of Social Media 10-802 in Spring 2011
  • Office hours: To be determined.

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.


Course Project