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  Likeology: Understanding, Predicting, and Aggregating Likes in Social Media


Dongwon Lee

Penn State University, USA

 


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Abstract

The recent dramatic increase in the usage and prevalence of social media has led to the creation and sharing of a significant amount of user generated contents (UGCs) in various formats. Users not only generate and access UGCs in social media, but also actively evaluate and interact with them by adding comments or expressing their preferences toward the UGCs.

In particular, recently, user preferences by means of the Like features have prevailed (e.g., LIKE in Facebook, +1 in Google+, re-pin in Pinterest, and favorite in Flickr). Despite such massive social media data with rich Like-like relationships therein, however, there has not been a dedicated tutorial that covered the diverse aspects of Likes. As understanding user preferences (via Likes) and providing personalized recommendation thereof in social media has keen implications to businesses, the topic of Likes has become increasingly important. 

In this tutorial, to address this important and timely topic, we aim to provide a tutorial, named as “Likeology” that presents a comprehensive overview of Likes in social media: e.g., how to model Likes, how to predict the evolution of Likes, and how to aggregate Likes.

Outline

The tutorial consists of three parts as follows:
  • Prelude
    • Part 1: Modeling Likes (40 min)
      • Values of Likes
      • Meanings of Likes              
      • Understanding Likes
    • Part 2: Predicting Likes (40 min)
      • What affects Likes            
      • Predicting Likes and Dislikes     
      • Predicting Like Count                 
    • Part 3: Aggregating Likes (40 min)
      • Likes as Ratings
      • Likes as Ranks
      • Recommending Likes                 
  • Postlude




Bio of speaker

Dongwon Lee, Associate Professor, Penn State University, USA (dongwon@psu.edu)

Dongwon Lee is an associate professor of the Pennsylvania State University, College of Information Sciences and Technology, USA. From Jan. 2015 to Dec. 2016, he has been also serving as a rotating program director at National Science Foundation (NSF). He obtained his Ph.D. in Computer Science from UCLA in 2002. Since joining Penn State in 2002, working mostly on the issues arising in the management and mining of data, he has (co-) authored over 130+ scholarly articles in selective publication outlets in Databases and Data Mining fields.