Evently: Modeling and Analyzing Reshare Cascades with Hawkes Processes
- Publication Type:
- Conference Proceeding
Open Access
Copyright Clearance Process
- Recently Added
- In Progress
- Open Access
This item is open access.
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.
Please use this identifier to cite or link to this item: