Linking Epidemic Models and Self-exciting Processes for Online and Offline Event Diffusions
Creators: name: Kong, Quyu
Publisher: name: The Australian National University
Year: 2022

description: Temporal diffusion data, which comprises time-stamped events, is ubiquitous, ranging from information diffusing in online social media platforms to infectious diseases spreading in offline communities. Pressing problems, such as predicting the popularity of online information and containing epidemics, demand temporal diffusion models for understanding, modeling, and controlling diffusion dynamics. This thesis discusses diffusions of online information and epidemics by developing and connecting self-exciting processes and epidemic models. First, we propose a novel dual mixture self-exciting process for characterizing online information diffusions related to online items, such as videos or news articles. By observing that maximum likelihood estimates are separable in a Hawkes process, the model, consisting of a Borel mixture model and a kernel mixture model, jointly learns the unfolding of a heterogeneous set of cascades. When applied to cascades of the same online items, the model directly characterizes their spread dynamics and provides interpretable quantities, such as content virality and content influence decay, as well as methods for predicting the final content popularities. On two retweet cascade datasets, we show that our models capture the differences between online items at the granularity of items, publishers, and categories. Next, we propose novel ideal strategies to explore the limits of both testing and contact tracing strategies, which have been shown effective in some epidemics (e.g., SARS) but ineffective in some others (e.g., COVID-19). We then develop a superspreading random contact network that accounts for the superspreading effect of infectious diseases, where several infected cases result in most secondary infections. In simulations, we observe gaps between ideal and standard strategies by examining extensive sets of epidemic parameters, highlighting the need to explore intelligent strategies. We also present a classification of different diseases based on how containable they are under different strategies. Then, we bridge epidemic models and self-exciting processes with a novel generalized stochastic Susceptible-Infected-Recovered (SIR) model with arbitrary recovery time distributions. We articulate the relationship between recovery time distributions, recovery hazard functions, and infection kernels of self-exciting processes. We also present methods for simulating, fitting, evaluating, and predicting the generalized process. On three large Twitter diffusion datasets, we show that the modeling performance of the infection kernels varies depending on the temporal structures of diffusions and user behavior, such as the likelihood of being bots. We further improve the prediction of popularity by combining two models identified as complementary in the goodness-of-fit tests. Last, we present evently, a tool for modeling online reshare cascades, particularly retweet cascades, using self-exciting processes. This tool fills in a gap between the practitioners of online social media analysis --- usually social, political, and communication scientists --- and the accessibility to tools capable of examining online discussions. It provides a comprehensive set of functionalities for processing raw data from Twitter public APIs, modeling the temporal dynamics of processed retweet cascades and characterizing online users with a wide range of diffusion measures. Overall, this thesis studies temporal diffusions of online information and epidemics by proposing novel epidemic models and self-exciting processes. It provides tools for predicting information popularities, characterizing online items, and classifying online item categories with state-of-the-art performances. It also contributes observations in applying testing and tracing strategies in containing epidemics. Lastly, evently facilitates temporal diffusion analysis for practitioners from various fields, such as social science and epidemiology.