On June 2nd, 2017, Xiauyan Lu presented a paper entitled "Predicting Viral News Events in Online Media" authored by himself and Prof. Boleslaw Szymanski at the IEEE Parallel and Distributed Proc. for Comput. Social Systems (ParSocial 2011).

On Friday, June 2nd, 2017, Xiauyan Lu presented a paper entitled "Predicting Viral News Events in Online Media" authored by himself and Prof. Boleslaw Szymanski at the IEEE Parallel and Distributed Processing for Computational Social Systems (ParSocial 2017), and published in Proc. IEEE International Parallel and Distributed Processing Symposium Workshops, Orlando, Florida, pp. 1447-1456 DOI 10.1109/IPDPSW.2017.82. The paper focuses on challenges of predicting the virality of cascades in social media. The main challenge the underlying information propagation topology is often hidden or incomplete because of the lack of explicit citations of the sources. We proposed a scalable parallel algorithm to derive the node embedding to better understand the information dissemination patterns and predict emergent cascades of viral events in online media. The parallel algorithm iteratively merges local node embedding in particular communities to obtain the global optimal results so that the processing of cascades can be significantly accelerated. Based on the obtained latent representation of nodes, the emergent cascades of viral news events in online media can be successfully predicted with an 80% accuracy at its early stage. The paper is available at the link above.