CSE 472/598, Fall 2015

Social Media Mining

Course Description:

Social media allows a passive reader (or an average Joe) to become an active producer (or a shining online star), creating a phenomenal landscape change in terms of Web-based activities. With various social media (Facebook, Twitter, Digg, del.licio.us, MySpace, StumbleUpon, etc.), people can share content, opinions, insights, experiences, perspectives, and media themselves, as well as producing many new media via techniques such as mashing up. Social networks emerge with the pervasive use of social media. We will briefly introduce the background of social computing including concepts and principles such as small world, random networks, scale-free networks, laws and distributions (normal distribution, Zipf’s law, power law), search in networks, propagation of influence and trust, diffusion (epidemics), robustness in networks, collective wisdom, collaborative filtering, social decision making, and the relationships between Long Tails and Short Heads.  We will also study techniques regarding the behavior of website visitors and the use of data collected from a web site to determine which aspects of the website work toward various objectives. For example, what are the patterns some online frequent purchasers? How can we keep a web surfer sufficiently long? What can make a person to come back? We will learn what representative data collection techniques are, and how to use collected data to help achieve tasks such as recommendation, cross reference, attention retention, and identifying key performance indicators.  We will learn representative approaches to data collection (log-file analysis, page tagging). Some examples of collectable statistics are hit, page view, visit/session, repeat visitor, new visitor, impression, topics, and singletons. We will study issues like public opinion, sentiment analysis, privacy, trust, and reputation. This course aims to introduce the state-of-the art developments in participatory Web techniques, social networks and analysis, network analysis and graph theory, information extraction, link analysis, and Web mining, to study emerging problems with social media, and to learn innovatively applying multidisciplinary approaches to problem solving. The ultimate goal is to sharpen problem solving skills of our senior and graduate students, and prepare them with this unique set of expertise for the increasing demands in IT industry and for in-depth advanced research.


  • CSE 471 or equivalent
  • CSE 310 Data Structures and Algorithms

Line numbers:

  • CSE472 - 82010
  • CSE598 - 92910

Classroom and Hours:

  • BYAC270, MW 9:00 – 10:15am

Office Hours:

    MW 10:15 - 11:15am, BYE 566
    Other times: by appointment only

  TA and Office Hours:

    Ghazaleh Beigi: TTh, 3:00 - 4:00pm, BYE 214, assisted by Dr. Uraz Yavanoglu

    co-lectured by Fred Morstatter, Project TAs Justin Sampson and Suhang Wang


Semester Duration: 8/20/2015 - 12/4/20145  Please send emails to TA  gbeigi@asu.edu for meetings outside office hours



Social Media Mining: An Introduction, Reza Zafarani, Mohammad Ali Abbasi, and Huan Liu, Cambridge University Press, 2014, free pdf download

Invited Lectures

Reading Assignments, Homework, Projects, and Mid-Term Exams (Tentative, Details at myASU):

Slides and Schedule:

Useful Links

We will include below interesting links recommended by our students and others.

Academic Integrity and Student Conduct

Last updated: 11/23/15
Maintained by Huan Liu

If you have comments or suggestions, email me at huan.liu at asu.edu