Demo Data from SIGIR 2026 / reviewers in the wild / expert
Hao Peng 0006
dblp:69/7742-6
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2019
0000-0002-5668-1680ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Web and social media mining · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational social science and digital humanities
collective behavior |
0.4 | 1 | 2019 | Event-Driven Analysis of Crowd Dynamics in the Black Lives Matter Online Social Movement · WWW 2019 |
Computational social science and digital humanities
social movement analysis |
0.4 | 1 | 2019 | Event-Driven Analysis of Crowd Dynamics in the Black Lives Matter Online Social Movement · WWW 2019 |
Web and social media mining › user behavior analysis
social interaction analysis |
0.1 | 1 | 2019 | Event-Driven Analysis of Crowd Dynamics in the Black Lives Matter Online Social Movement · WWW 2019 |
Methods — techniques the papers use, named apart from their topics
temporal analysis · 0.8event-driven analysis · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Event-Driven Analysis of Crowd Dynamics in the Black Lives Matter Online Social MovementabstractOnline 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. Hao Peng 0006, Ceren Budak, Daniel M. Romero |
WWW | 1 |