Swapnil Mishra
ABSTRACT
Understanding and predicting the popularity of online items is an important open problem in social media analysis. Most of the recent work on popularity prediction is either based on learning a variety of features from full network data or using generative processes to model the event time data. We identify two gaps in the current state of the art prediction models. The first is the unexplored connection and comparison between the two aforementioned approaches. In our work, we bridge gap between feature-driven and generative models by modelling social cascade with a marked Hawkes self-exciting point process. We then learn a predictive layer on top for popularity prediction using a collection of cascade history. Secondly, the existing methods typically focus on a single source of external influence, whereas for many types of online content such as YouTube videos or news articles, attention is driven by multiple heterogeneous sources simultaneously - e.g. microblogs or traditional media coverage. We propose a recurrent neural network based model for asynchronous streams that connects multiple streams of different granularity via joint inference. We further design two new measures, one to explain the viral potential of videos, the other to uncover latent influences including seasonal trends. This work provides accurate and explainable popularity predictions, as well as computational tools for content producers and marketers to allocate resources for promotion campaigns.
- Justin Cheng, Lada Adamic, P Alex Dow, Jon Michael Kleinberg, and Jure Leskovec. 2014. Can cascades be predicted?. In WWW '14 . Google Scholar
Digital Library
- Riley Crane and Didier Sornette. 2008. Robust dynamic classes revealed by measuring the response function of a social system . PNAS '08, Vol. 105 (2008).Google ScholarCross Ref
- Swapnil Mishra, Marian-Andrei Rizoiu, and Lexing Xie. 2016. Feature Driven and Point Process Approaches for Popularity Prediction. In CIKM '16 . Google ScholarDigital Library
- Swapnil Mishra, Marian-Andrei Rizoiu, and Lexing Xie. 2018. Modeling Popularity in Asynchronous Social Media Streams with Recurrent Neural Networks. In ICWSM '18 .Google Scholar
- Marian-Andrei Rizoiu, Young Lee, Swapnil Mishra, and Lexing Xie. 2018a. Frontiers of Multimedia Research. ACM and M&C, Chapter Hawkes Processes for Events in Social Media. Google ScholarDigital Library
- Marian-Andrei Rizoiu, Swapnil Mishra, Quyu Kong, Mark Carman, and Lexing Xie. 2018b. SIR-Hawkes: Linking Epidemic Models and Hawkes Processes to Model Diffusions in Finite Populations. In WWW '18 . Google ScholarDigital Library
- Marian-Andrei Rizoiu, Lexing Xie, Scott Sanner, Manuel Cebrian, Honglin Yu, and Pascal Van Hentenryck. 2017. Expecting to be HIP: Hawkes Intensity Processes for Social Media Popularity. In WWW'17 . Google ScholarDigital Library
- Qingyuan Zhao, Murat A Erdogdu, Hera Y He, Anand Rajaraman, and Jure Leskovec. 2015. SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity. In KDD '15 . Google ScholarDigital Library
Index Terms
- Bridging Models for Popularity Prediction on Social Media
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