CSE 472, Fall 2018

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, Instagram, Snapchat etc.), people can share content, opinions, insights, experiences, perspectives, and media themselves, as well as producing many new media. 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), social recommendation, collective wisdom, collaborative filtering.  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 study issues like public opinion, sentiment analysis, privacy, trust, and reputation. This course aims to introduce the state-of-the art developments in participatory social media techniques, social networks and analysis, network analysis and graph theory, information extraction, link analysis, and social media mining, to study emerging problems, and to learn innovatively applying multidisciplinary approaches to problem solving. The ultimate goal is to sharpen problem solving and critical thinking 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.

Prerequisites:

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

Line numbers:

  • CSE472 - 93613

Note: Graduate students (PhD or Masters) can take this course toward their total credits

Classroom and Hours:

  • BYAC110, MW 9:00 – 10:15am
 

Office Hours:

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

  TA and Office Hours:

    TBA: TTh, 3:00 - 4:00pm, BYE xxx,

    co-lectured by , Project TAs 

   

Semester Duration: 8/16/2018 - 11/30/2018  Please send emails to TA TBA for meetings outside office hours

 


Textbook: 

Social Media Mining: An Introduction, Reza Zafarani, Mohammad Ali Abbasi, and Huan Liu, Cambridge University Press, (ISBN: 9781107018853), 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: 04/09/18
Maintained by Huan Liu


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