Utilizing Microblogs for Assisting Post-Disaster Relief Operations via Matching Resource Needs and Availabilities
Not on the map. Why?
Abstract (arXiv)
During a disaster event, two types of information that are especially useful for coordinating relief operations are needs and availabilities of resources (e.g., food, water, medicines) in the affected region. Information posted on microblogging sites is increasingly being used for assisting post-disaster relief operations. In this context, two practical challenges are (i)~to identify tweets that inform about resource needs and availabilities (termed as need-tweets and availability-tweets respectively), and (ii)~to automatically match needs with appropriate availabilities. While several works have addressed the first problem, there has been little work on automatically matching needs with availabilities. The few prior works that attempted matching only considered the resources, and no attempt has been made to understand other aspects of needs/availabilities that are essential for matching in practice. In this work, we develop a methodology for understanding five important aspects of need-tweets and availability-tweets, including what resource and what quantity is needed/available, the geographical location of the need/availability, and who needs / is providing the resource. Understanding these aspects helps us to address the need-availability matching problem considering not only the resources, but also other factors such as the geographical proximity between the need and the availability. To our knowledge, this study is the first attempt to develop methods for understanding the semantics of need-tweets and availability-tweets. We also develop a novel methodology for matching need-tweets with availability-tweets, considering both resource similarity and geographical proximity. Experiments on two datasets corresponding to two disaster events, demonstrate that our proposed methods perform substantially better matching than those in prior works.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Jul 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 1; 2023: 2; 2024: 3; 2025: 2.
8 papers.
- 2022Location reference recognition from texts: A survey and comparisoncited by 17
- 2023CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media textscited by 7
- 2024CReMa: Crisis Response through Computational Identification and Matching of Cross-Lingual Requests and Offers Shared on Social Mediacited by 4
- 2023PORTRAIT: a hybrid aPproach tO cReate extractive ground-TRuth summAry for dIsaster evenTcited by 3
- 2025Detecting Actionable Requests and Offers on Social Media During Crises Using LLMscited by 3
- 2024Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Textscited by 1
- 2024ADSumm: Annotated Ground-truth Summary Datasets for Disaster Tweet Summarizationcited by 0
- 2025"Actionable Help" in Crises: A Novel Dataset and Resource-Efficient Models for Identifying Request and Offer Social Media Postscited by 0
- 2025Detecting Actionable Requests and Offers on Social Media During Crises Using LLMs22 Apr 2025
- 2025"Actionable Help" in Crises: A Novel Dataset and Resource-Efficient Models for Identifying Request and Offer Social Media Posts24 Feb 2025
- 2024CReMa: Crisis Response through Computational Identification and Matching of Cross-Lingual Requests and Offers Shared on Social Media20 May 2024
- 2024ADSumm: Annotated Ground-truth Summary Datasets for Disaster Tweet Summarization10 May 2024
- 2024Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts25 Mar 2024
- 2023CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts11 Sep 2023
- 2023PORTRAIT: a hybrid aPproach tO cReate extractive ground-TRuth summAry for dIsaster evenT19 May 2023
- 2022Location reference recognition from texts: A survey and comparison4 Jul 2022