CSE509 Lecture 5

Editor's Notes

• #4: The past decade has witnessed the emergence of participatory Web and social media, bringing peopletogether in many creative ways. Millions of users are playing, tagging, working, and socializingonline, demonstrating new forms of collaboration, communication, and intelligence that were hardlyimaginable just a short time ago. Social media also helps reshape business models, sway opinions andemotions, and opens up numerous possibilities to study human interaction and collective behavior inan unparalleled scale. This lecture, from a data mining perspective, introduces characteristics of socialmedia, reviews representative tasks of computing with social media, and illustrates associated challenges.
• #8: In traditional media such as TV, radio, movies, and newspapers, it is only a small numberof “authorities” or “experts” who decide which information should be produced and how it is distributed.The majority of users are consumers who are separated from the production process. Thecommunication pattern in the traditional media is one-way traffic, from a centralized producer towidespread consumers.This new type of mass publication enables the production of timely news and grassrootsinformation and leads to mountains of user-generated contents, forming the wisdom of crowds
• #9: Twitter: a directed graphFacebook: an undirected graphIn Twitter, for example, one user x follows another user y, but user y does not necessarily follow user x. In this case, the follower-followee network is directed and asymmetrical
• #10: a linear relationship between the logarithms of the variables
• #11: the number of connections between one’s friends over the total number of possible connections among them
• #12: Previously: email communication networks, instant messaging networks, mobile call networks, friendshipNetworks. Other forms of complex networks, like coauthorship or citation networks, biological networks, metabolic pathways, genetic regulatory networks and food webThese large-scale networks combined with unique characteristics of social media present novelchallenges for mining social media.In reality, multiple relationships can exist between individuals. Two personscan be friends and colleagues at the same time. Thus, a variety of interactions exist betweenthe same set of actors in a network. Multiple types of entities can also be involved in onenetwork. For many social bookmarking and media sharing sites, users, tags and content areintertwined with each other, leading to heterogeneous entities in one network. Analysis ofthese heterogeneous networks involving heterogeneous entities or interactions requires newtheories and tools.Social media emphasizes timeliness. For example, in content sharing sites andblogosphere, people quickly lose their interest in most shared contents and blog posts. Thisdiffers fromclassical web mining.Newusers join in,newconnections establish between existingmembers, and senior users become dormant or simply leave.How can we capture the dynamicsof individuals in networks? Can we find the die-hard members that are the backbone ofcommunities? Can they determine the rise and fall of their communities?In social media, people tend to share their connections. The wisdomof crowds, in forms of tags, comments, reviews, and ratings, is often accessible. The metainformation, in conjunction with user interactions, might be useful for many applications.It remains a challenge to effectively employ social connectivity information and collectiveintelligence to build social computing applications.A research barrier concerning mining social media is evaluation. In traditionaldata mining, we are so used to the training-testing model of evaluation. It differs in socialmedia. Since many social media sites are required to protect user privacy information, limitedbenchmark data is available. Another frequently encountered problem is the lack of groundtruth for many social computing tasks, which further hinders some comparative study ofdifferent works.Without ground truth, how can we conduct fair comparison and evaluation?Slide 7-11