Paper Info
Title
Event-Driven Analysis of Crowd Dynamics in the Black Lives Matter Online Social Movement
Abstract
Online social movements (OSMs) play a key role in promoting democracy in modern society. Most online activism is largely driven by critical offline events. Among many studies investigating collective behavior in OSMs, few has explored the interaction between crowd dynamics and their offline context. Here, focusing on the Black Lives Matter OSM and utilizing an event-driven approach on a dataset of 36 million tweets and thousands of offline events, we study how different types of offline events-police violence and heightened protests-influence crowd behavior over time. We find that police violence events and protests play important roles in the recruitment process. Moreover, by analyzing the re-participation dynamics and patterns of social interactions, we find that, in the long term, users who joined the movement during police violence events and protests show significantly more commitment than those who joined during other times. However, users recruited during other times are more committed to the movement than the other two groups in the short term. Furthermore, we observe that social ties formed during police violence events are more likely to be sustained over time than those formed during other times. Contrarily, ties formed during protests are the least likely to be maintained. Altogether, our results shed light on the impact of bursting events on the recruitment, retention, and communication patterns of collective behavior in the Black Lives Matter OSM.
Year
DOI
Venue
2019
10.1145/3308558.3313673
WWW '19: The Web Conference San Francisco CA USA May, 2019
Keywords
Field
DocType
black lives matter, online social movement, social media
Collective behavior,World Wide Web,Internet privacy,Social movement,Social media,Computer science,Crowd dynamics,Democracy,Interpersonal ties,Crowd psychology
Conference
ISBN
Citations 
PageRank 
978-1-4503-6674-8
1
0.35
References 
Authors
0
3
Name
Order
Citations
PageRank
Hao Peng110.35
Ceren Budak21096.60
Daniel M. Romero31526134.33