[Seminar] Predicting Content Propagation in Social Media
■호스트: 권태경 교수(9105, 880-9105)
The word-of-mouth diffusion has been regarded as an important mechanism to advertise a new idea, image, technology, or product in online social networks (OSNs). In this talk, I will introduce our recent research work on content propagation in Pinterest, a popular OSN where people can collect, organize, and share images that they find interesting. We propose models to predict whether a content will be (a) popular in terms of the volume of its cascade, or (b) viral in terms of the structural virality. I will also briefly introduce our work on conversation cascade in Reddit. We believe our work can provide an important insight for for content providers, OSN operators, and marketers in predicting popular or viral content diffusion.
Jinyoung Han is an assistant professor at Hanyang University, ERICA Campus. He was a postdoctoral researcher at University of California, Davis, and Seoul National University (SNU). He received his B.S. degree in computer science from KAIST in 2007 and Ph.D. degree in computer science and engineering from SNU in 2013. He has published over 30 papers in major conferences and journals including ACM SIGMETRICS, IEEE INFOCOM, World Wide Web (WWW), ACM COSN, ICWSM, IEEE ICDCS and ACM TWEB. He has been served as Technical Program Committees members in the top conferences including IEEE INFOCOM and WWW conferences. His research interests include data science, network science, social computing, communications, business analytics, and future Internet.