CSE 472, Fall 2024

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, Mastodon, 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. Lately, people also use large language models (LLMs) or Generative AI to create and produce social media contents. 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.

Academic Integrity https://engineering.asu.edu/integrity/

Prerequisites:

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

Line numbers:

  • CSE472 - 69174

Note: Graduate students (PhD or Masters) can take this course toward their total credits; send me email if you're told otherwise (by whom)

Classroom and Hours:

  • CAVC 359, TTh 4:30PM – 5:45PM
SYLLABUS available at my.asu.edu

Office Hours:

TTh 5:45PM - 6:45PM, in Classroom or BYE 566


Other times: by appointment only

  TA and Office Hours:

  Ali Beigi, MW 1:00pm - 2:00pm BYENG 221,   abeigi at asu.edu, or by appointment

   Volunteer TAs: Bohan Jiang, Saketh Vishnubhatla

  Guest Lecturers - Amrita Bhattacharjee

   Invited Speakers: Dr. Yilin Wang Adobe Research; Professor Raha Moraffah WPI; Professor Edward Chang Stanford;

   Project Mentors - Amrita Bhattacharjee, Tharindu Kumarage

   

Semester Duration: 8/22/2024 - 12/06/2024  Please send emails to our TA 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 Talks

 

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

Created on 6/10/24 and updated based on needs
Maintained by Huan Liu


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