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.
TTh 3:00 - 4:00pm, BYE 566
TA and Office Hours:
Reza Zafarani: Mondays and Wednsdays, 3:00 - 4:00pm, BYE 221
Ali Abbasi: TBA
|Please send emails to the TAs for meetings|
We will mainly use lecture notes (powerpoint slides) to provide a central repository including materials from various reference books. More will be discussed in our first class.
Networks, Crowds, and Markets: Reasoning About a Highly Connected World, (available in pdf), David Easley and Jon Kleinberg, Cambridge University Press 2010.
Networks: An Introduction, Mark Newman, Oxford University Press, 2010.
Community Detection and Mining in Social Media, Morgan & Claypool, 2010.
Modeling and Data Mining in Blogosphere, Morgan & Claypool, 2009.
Web Data Mining - Exploring Hyperlinks, Contents, and Usage Data, Bing Liu, Springer, 2007
Mining the Web - Discovering Knowledge from Hypertext Data, Soumen Chakrabarti, Morgan Kaufmann, 2003
Social Network Analysis - Methods and Applications, Stanley Wasserman and Katherine Faust, Cambridge, 1994
We will include below interesting links recommended by our students and others.
Last updated: 05/11/11
Maintained by Huan Liu
If you have comments or suggestions, email me at
huan.liu at asu.edu
huan.liu at asu.edu