LIKEs-R-Us: Analyzing LIKEs in Social Media

Speaker:  Dongwon Lee – State College, PA, United States
Topic(s):  Information Systems, Search, Information Retrieval, Database Systems, Data Mining, Data Science

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 information in various formats such as texts, photos, or videos. When it comes to information consumption, people are not only accessing or appreciating published and shared contents, but also interacting with them by adding comments or pressing the Like button (or expressing other relationships similar to Like in nature such as “LIKE” in Facebook, “+1” in Google+, “re-pin” in Pinterest, and “favorite” in Flickr). With such massive social media data with rich LIKE-like relationships therein, in addition, recommendation has been proven to be effective in mitigating the information overload problem. It has demonstrated its strength in improving the quality of user experience, and positively impacted the success of social media. In this introductory talk, therefore, I present various examples of LIKE in social media, existing literatures studying about LIKE in social media, the analysis and modeling of LIKE activities, techniques to predict the creation and deletion of LIKE relationship, and to aggregate and recommend new LIKE relationships towards users and items in social media. 

About this Lecture

Number of Slides:  60
Duration:  60 minutes
Languages Available:  English
Last Updated: 

Request this Lecture

To request this particular lecture, please complete this online form.

Request a Tour

To request a tour with this speaker, please complete this online form.

All requests will be sent to ACM headquarters for review.