Identifying crisis-related informative tweets using learning on distributions
作者：
Highlights：
• Social network such as Twitter are valuable tools for sharing and informing others about an ongoing crisis.
• Identifying crisis-related informative tweets is an essential tool for authorities to respond quickly to crisis.
• Distributional hypothesis states that meaning similarity and distributional similarity are correlated.
• Based on distributional hypothesis, each crisis-related tweet can be considered as a “distribution”.
• Using the recent development in machine learning, namely, learning on distributions, each object of learning can be considered as a distribution.
• Learning on distributions achieves very good results in identifying informative tweets about a crisis incident.
摘要
•Social network such as Twitter are valuable tools for sharing and informing others about an ongoing crisis.•Identifying crisis-related informative tweets is an essential tool for authorities to respond quickly to crisis.•Distributional hypothesis states that meaning similarity and distributional similarity are correlated.•Based on distributional hypothesis, each crisis-related tweet can be considered as a “distribution”.•Using the recent development in machine learning, namely, learning on distributions, each object of learning can be considered as a distribution.•Learning on distributions achieves very good results in identifying informative tweets about a crisis incident.
论文关键词：Crisis management,Crisis incidents tweets,Learning on distributions
论文评审过程：Received 31 March 2019, Revised 12 October 2019, Accepted 13 October 2019, Available online 2 November 2019, Version of Record 2 November 2019.
论文官网地址：https://doi.org/10.1016/j.ipm.2019.102145
