=Paper=
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|pdfUrl=https://ceur-ws.org/Vol-1893/InvitedTalk.pdf
|volume=Vol-1893
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==None==
<pdf width="1500px">https://ceur-ws.org/Vol-1893/InvitedTalk.pdf</pdf>
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Proceedings of the 3rd International Workshop on Social Influence Analysis (SocInf 2017)
August 19th, 2017 - Melbourne, Australia




   Invited Talk

   Measuring and Modeling Popularity in Social Media
   Prof. Lexing Xie
   Research School of Computer Science at the Australian National University


   Abstract
   Attention is a scarce resource in the modern world, understanding and predicting attention
   allocation in online crowd is an important open research challenge. This talk will cover a few
   recent results from my group on understanding and predicting popularity. I will start by
   describing a unique longitudinal measurement study on video popularity history, and introduce
   popularity phases, a novel way to describe the evolution of popularity over time. I will then
   introduce the methodology of stochastic point processes, with which we model tweeting
   behavior over time, and extend to model volumes of attention. I will then discuss a physics-
   inspired stochastic model that connects exogenous stimuli and endogenous responses to
   explain and forecast popularity. With such novel representation and new models, we can
   correlate video content type to popularity patterns, make better predictions, describe the endo-
   exo factors driving popularity, and forecast the effects of promotion campaigns.

   .

                                     Biographical Sketch
                                     Lexing Xie is Associate Professor in the Research School
                                     of Computer Science at the Australian National University,
                                     she leads the ANU Computational Media lab
                                     (http://cm.cecs.anu.edu.au). Her research interests are in
                                     machine learning, multimedia, social media. Of particular
                                     recent interest are stochastic time series models, neural
                                     network for sequences, and active learning, applied to
                                     diverse problems such as multimedia knowledge graphs,
                                     modeling popularity in social media, joint optimization and
                                     structured     prediction      problems,      and     social
                                     recommendation. Her research is supported from the US
                                     Air Force Office of Scientific Research, Data61, Data to
   Decisions CRC and the Australian Research Council. Lexing's research has received six best
   student paper and best paper awards in ACM and IEEE conferences between 2002 and 2015.
   She is IEEE Circuits and Systems Society Distinguished Lecturer 2016-2017. She currently
   serves an associate editor of ACM Trans. MM, ACM TiiS and PeerJ Computer Science. Her
   service roles include the program and organizing committees of major multimedia, machine
   learning, web and social media conferences. She was research staff member at IBM T.J.
   Watson Research Center in New York from 2005 to 2010, and adjunct assistant professor at
   Columbia University 2007-2009. She received B.S. from Tsinghua University, Beijing, China,
   and M.S. and Ph.D. degrees from Columbia University, all in Electrical Engineering.




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