An Empirical Methodology for Detecting and Prioritizing Needs during Crisis Events
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Prompt Tuning Vision Language Models with Margin Regularizer for Few-Shot Learning under Distribution Shifts
LLM-BABYBENCH: Understanding and Evaluating Grounded Planning and Reasoning in LLMs
1$^{st}$ Place Solution of WWW 2025 EReL@MIR Workshop Multimodal CTR Prediction Challenge
Feature Fusion Revisited: Multimodal CTR Prediction for MMCTR Challenge
Quadratic Interest Network for Multimodal Click-Through Rate Prediction
FinBERT-QA: Financial Question Answering with pre-trained BERT Language Models
토론
이 논문과 연결된 포럼 글이 아직 없습니다.