An Empirical Methodology for Detecting and Prioritizing Needs during Crisis Events
This addresses the challenge for crisis responders in efficiently extracting actionable information from noisy social media data, though it appears incremental as it builds on existing needs detection tasks.
The study tackled the problem of detecting and prioritizing essential needs from social media during crises by proposing two novel methods for identifying ranked resource lists and who-needs-what sentences, achieving 64% precision for top needs and 68% F1-score for who-needs-what detection on COVID-19 tweets.
In times of crisis, identifying the essential needs is a crucial step to providing appropriate resources and services to affected entities. Social media platforms such as Twitter contain vast amount of information about the general public's needs. However, the sparsity of the information as well as the amount of noisy content present a challenge to practitioners to effectively identify shared information on these platforms. In this study, we propose two novel methods for two distinct but related needs detection tasks: the identification of 1) a list of resources needed ranked by priority, and 2) sentences that specify who-needs-what resources. We evaluated our methods on a set of tweets about the COVID-19 crisis. For task 1 (detecting top needs), we compared our results against two given lists of resources and achieved 64% precision. For task 2 (detecting who-needs-what), we compared our results on a set of 1,000 annotated tweets and achieved a 68% F1-score.