Social media crowdsourcing for rapid damage assessment following a sudden-onset natural hazard event
作者：
Highlights：
• Develop a crowdsourcing approach using Twitter data for rapid earthquake damage assessment.
• Build text classification models to parse the damage levels adapated from MMI Scale.
• Create application-specific library for earthquake damage assessment.
• The time of saturation (convergence) for post-event damage assessment was 14–16 h.
• The social media derived geographic damage distribution appears consistent with the USGS MMI “Did You Feel It” map.
摘要
•Develop a crowdsourcing approach using Twitter data for rapid earthquake damage assessment.•Build text classification models to parse the damage levels adapated from MMI Scale.•Create application-specific library for earthquake damage assessment.•The time of saturation (convergence) for post-event damage assessment was 14–16 h.•The social media derived geographic damage distribution appears consistent with the USGS MMI “Did You Feel It” map.
论文关键词：Sudden-onset hazard,Damage assessment,Social media,Crowdsourcing,Text classification
论文评审过程：Received 27 November 2019, Revised 30 May 2021, Accepted 30 May 2021, Available online 8 June 2021, Version of Record 8 June 2021.
论文官网地址：https://doi.org/10.1016/j.ijinfomgt.2021.102378
