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/
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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:
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SYLLABUS available at my.asu.edu |
Office Hours:TTh 5:45PM - 6:45PM, in Classroom or BYE 566
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TA and Office Hours:Ali Beigi, MW 11:00am - 12:00pm BYENG 221, abeigi at asu.edu, or by appointment Volunteer TAs: Ujun Jeong, Bohan Jiang Guest Lecturers - Raha Moraffah, Amrita Bhattacharjee Invited Speakers: Tianlong Chen, PhD Project Mentors - Amrita Bhattacharjee, Tharindu Kumarage |
| Semester Duration: 8/17/2023 - 12/01/2023 | Please send emails to our TA for meetings outside office hours |
Social Media Mining: An Introduction, Reza Zafarani, Mohammad Ali Abbasi, and Huan Liu, Cambridge University Press, (ISBN: 9781107018853), 2014, free pdf download
We will include below interesting links recommended by our students and others.
Created on
6/7/23 and updated based on needs
Maintained by Huan Liu
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