Automatic identification of eyewitness messages on twitter during disasters
Social media platforms such as Twitter provide convenient ways to share and consume important information during disasters and emergencies. Information from bystanders and eyewitnesses can be useful for law enforcement agencies and humanitarian organizations to get firsthand and credible information about an ongoing situation to gain situational awareness among other potential uses. However, the identification of eyewitness reports on Twitter is a challenging task. This work investigates different types of sources on tweets related to eyewitnesses and classifies them into three types (i) direct eyewitnesses, (ii) indirect eyewitnesses, and (iii) vulnerable eyewitnesses. Moreover, we investigate various characteristics associated with each kind of eyewitness type. We observe that words related to perceptual senses (feeling, seeing, hearing) tend to be present in direct eyewitness messages, whereas emotions, thoughts, and prayers are more common in indirect witnesses. We use these characteristics and labeled data to train several machine learning classifiers. Our results performed on several real-world Twitter datasets reveal that textual features (bag-of-words) when combined with domain-expert features achieve better classification performance. Our approach contributes a successful example for combining crowdsourced and machine learning analysis, and increases our understanding and capability of identifying valuable eyewitness reports during disasters.
Other Information
Published in: Information Processing & Management
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
See article on publisher's website: https://dx.doi.org/10.1016/j.ipm.2019.102107
History
Language
- English
Publisher
ElsevierPublication Year
- 2020
License statement
This Item is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.Institution affiliated with
- Hamad Bin Khalifa University
- Qatar Computing Research Institute - HBKU