Document Type
Article
Source Publication Title
Proceedings of the 17th ACM International Conference on Web Search and Data Mining
DOI
https://dl.acm.org/doi/10.1145/3616855.3635704
Abstract
We present Wildfire, an innovative social sensing platform designed for laypersons. The goal is to support users in conducting social sensing tasks using Twitter data without programming and data analytics skills. Existing open-source and commercial social sensing tools only support data collection using simple keyword-based or account-based search. On the contrary, Wildfire employs a heuristic graph exploration method to selectively expand the collected tweet-account graph in order to further retrieve more task-relevant tweets and accounts. This approach allows for the collection of data to support complex social sensing tasks that cannot be met with a simple keyword search. In addition, Wildfire provides a range of analytic tools, such as text classification, topic generation, and entity recognition, which can be crucial for tasks such as trend analysis. The platform also provides a web-based user interface for creating and monitoring tasks, exploring collected data, and performing analytics.
Publication Date
3-4-2024
Language
English
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Zhang, Zeyu; Zhu, Zhengyuan; Zhang, Haiqi; Patel, Foram; Caraballo, Josue; Hennecke, Patrick; and Li, Chengkai, "Wildfire: A Twitter Social Sensing Platform for Layperson" (2024). Association of Computing Machinery Open Access Agreement Publications-Archive. 83.
https://mavmatrix.uta.edu/utalibraries_acmoapubs/83