Toward a Social Media Emergency Events Detection Based on Supervised Learning

2021 ◽  
pp. 265-274
Author(s):  
Yahir Mendoza ◽  
Jorge Santillan ◽  
Roberth Alcivar-Cevallos ◽  
Jorge Parraga-Alava
Keyword(s):  
Social Media ◽  
Supervised Learning ◽  
Emergency Events ◽  
Events Detection

Rapid Perception of Public Opinion in Emergency Events through Social Media

2022 ◽  
Vol 23 (2) ◽  
Author(s):  
Yudi Chen ◽  
Yun Li ◽  
Zifu Wang ◽  
Alma Joanna Quintero ◽  
Chaowei Yang ◽  
...  
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Emergency Events

Crowdsourcing Incident Information for Emergency Response using Open Data Sources in Smart Cities

2018 ◽  
Vol 2672 (1) ◽  
pp. 198-208 ◽  
Author(s):  
Fan Zuo ◽  
Abdullah Kurkcu ◽  
Kaan Ozbay ◽  
Jingqin Gao
Keyword(s):  
Social Media ◽  
Prior Knowledge ◽  
Emergency Response ◽  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Smart Cities ◽  
Hurricane Sandy ◽  
Support Vector ◽  
Twitter Data ◽  
Emergency Events

Emergency events affect human security and safety as well as the integrity of the local infrastructure. Emergency response officials are required to make decisions using limited information and time. During emergency events, people post updates to social media networks, such as tweets, containing information about their status, help requests, incident reports, and other useful information. In this research project, the Latent Dirichlet Allocation (LDA) model is used to automatically classify incident-related tweets and incident types using Twitter data. Unlike the previous social media information models proposed in the related literature, the LDA is an unsupervised learning model which can be utilized directly without prior knowledge and preparation for data in order to save time during emergencies. Twitter data including messages and geolocation information during two recent events in New York City, the Chelsea explosion and Hurricane Sandy, are used as two case studies to test the accuracy of the LDA model for extracting incident-related tweets and labeling them by incident type. Results showed that the model could extract emergency events and classify them for both small and large-scale events, and the model’s hyper-parameters can be shared in a similar language environment to save model training time. Furthermore, the list of keywords generated by the model can be used as prior knowledge for emergency event classification and training of supervised classification models such as support vector machine and recurrent neural network.

Building the Multi-Modal Storytelling of Urban Emergency Events Based on Crowdsensing of Social Media Analytics

2016 ◽  
Vol 22 (2) ◽  
pp. 218-227 ◽  
Author(s):  
Zheng Xu ◽  
Yunhuai Liu ◽  
Hui Zhang ◽  
Xiangfeng Luo ◽  
Lin Mei ◽  
...  
Keyword(s):  
Social Media ◽  
Social Media Analytics ◽  
Emergency Events ◽  
Urban Emergency Events

scholarly journals Developing a supervised learning-based social media business sentiment index

2019 ◽  
Vol 76 (5) ◽  
pp. 3882-3897 ◽  
Author(s):  
Hyeonseo Lee ◽  
Nakyeong Lee ◽  
Harim Seo ◽  
Min Song
Keyword(s):  
Social Media ◽  
Supervised Learning

Building knowledge base of urban emergency events based on crowdsourcing of social media

2016 ◽  
Vol 28 (15) ◽  
pp. 4038-4052 ◽  
Author(s):  
Zheng Xu ◽  
Hui Zhang ◽  
Chuanping Hu ◽  
Lin Mei ◽  
Junyu Xuan ◽  
...  
Keyword(s):  
Social Media ◽  
Knowledge Base ◽  
Emergency Events ◽  
Urban Emergency Events

Constrained NMF-based semi-supervised learning for social media spammer detection

2017 ◽  
Vol 125 ◽  
pp. 64-73 ◽  
Author(s):  
Dingguo Yu ◽  
Nan Chen ◽  
Frank Jiang ◽  
Bin Fu ◽  
Aihong Qin
Keyword(s):  
Social Media ◽  
Supervised Learning
Sign in / Sign up

Export Citation Format

Share Document