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 handling large quantities of online data, and examining online users and their behavior. We present two tools,birdspotter and evently, for analyzing online users based on their involvement in retweet cascades . birdspotter provides a toolkit to measure social influence and botnets of Twitter users. While it leverages the multimodal information of tweets, such as text contents, evently augments the user measurement by modeling the temporal dynamics of information diffusions using self-exciting processes. Both tools are designed for users with a wide range of computer expertise and include tutorials and detailed documentation. We illustrate a case study of a topical dataset relating to COVID-19 , using both tools for end-to-end analysis of onlineuser behavior .
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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handling large quantities of online data, and examining online users and their behavior. We present two tools,birdspotter and evently, for analyzing online users based on their involvement in retweet cascades
examining online discussions of users
R
birdspotter provides a toolkit to measure social influence and botnets of Twitter users. While it leverages the multimodal information of tweets, such as text contents, evently augments the user measurement by
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,
R
information diffusions using self-exciting processes. Both tools are designed for users
processed retweet cascades and characterizing online users with a wide range of diffusion measures. This tool is designed for researchers
R
and include
, and it includes
R
a case study of
the usage of evently with an end-to-end analysis of online user behavior on
R
, using both tools for end-to-end analysis of onlineuser behavior
. We show that, by characterizing users solely based on how their content spreads online, we can disentangle influential users and online bots