Evently: Modeling and Analyzing Reshare Cascades with Hawkes Processes

• URL: http://arxiv.org/abs/2006.06167v3
• Date: Fri, 8 Jan 2021 12:14:23 GMT
• Title: Evently: Modeling and Analyzing Reshare Cascades with Hawkes Processes
• Authors: Quyu Kong, Rohit Ram and Marian-Andrei Rizoiu
• Abstract summary: Evently is a tool for modeling online reshare cascades and particularly retweet cascades. It provides a comprehensive set of functionalities for processing raw data from Twitter public APIs. We show that, by characterizing users solely based on how their content spreads online, we can disentangle influential users and online bots.
• Score: 12.558187319452657
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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