Evently: Modeling and Analyzing Reshare Cascades with Hawkes Processes
Abstract
Modeling online discourse dynamics is a core activity in understanding the spread of information, both offline and online, and emergent online behavior. There is currently a disconnect between the practitioners of online social media analysis -- usually social, political and communication scientists -- and the accessibility to tools capable of examining online discussions of users. Here we present evently, a tool for modeling online reshare cascades, and particularly retweet cascades, using self-exciting processes. 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. This tool is designed for researchers with a wide range of computer expertise, and it includes tutorials and detailed documentation. We illustrate the usage of evently with an end-to-end analysis of online user behavior on a topical dataset relating to COVID-19. We show that, by characterizing users solely based on how their content spreads online, we can disentangle influential users and online bots.
Cite
@article{arxiv.2006.06167,
title = {Evently: Modeling and Analyzing Reshare Cascades with Hawkes Processes},
author = {Quyu Kong and Rohit Ram and Marian-Andrei Rizoiu},
journal= {arXiv preprint arXiv:2006.06167},
year = {2021}
}
Comments
WSDM 2021 Demo