TY - GEN
T1 - Evently
T2 - 14th ACM International Conference on Web Search and Data Mining, WSDM 2021
AU - Kong, Quyu
AU - Ram, Rohit
AU - Rizoiu, Marian Andrei
N1 - Publisher Copyright:
© 2021 Owner/Author.
PY - 2021/8/3
Y1 - 2021/8/3
N2 - 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.
AB - 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.
KW - hawkes processes
KW - information diffusion
KW - open source software
KW - reshare cascades
UR - http://www.scopus.com/inward/record.url?scp=85103066233&partnerID=8YFLogxK
U2 - 10.1145/3437963.3441708
DO - 10.1145/3437963.3441708
M3 - Conference contribution
T3 - WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining
SP - 1097
EP - 1100
BT - WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining
PB - Association for Computing Machinery, Inc
Y2 - 8 March 2021 through 12 March 2021
ER -