Automatic Identification and Ranking of Emergency Aids in Social Media Macro Community

TL;DR

This paper presents a neural network-based model for retrieving and ranking critical disaster relief information from multilingual short texts on social media, improving aid response during emergencies.

Contribution

It introduces a novel embedding and ranking approach tailored for short, multilingual disaster-related tweets, with a new weighted ranking algorithm for relevance.

Findings

Achieved 6.81% mean average precision on disaster relief tweets

Developed a model handling multilingual short texts effectively

Demonstrated improved retrieval accuracy in disaster scenarios

Abstract

Online social microblogging platforms including Twitter are increasingly used for aiding relief operations during disaster events. During most of the calamities that can be natural disasters or even armed attacks, non-governmental organizations look for critical information about resources to support effected people. Despite the recent advancement of natural language processing with deep neural networks, retrieval and ranking of short text becomes a challenging task because a lot of conversational and sympathy content merged with the critical information. In this paper, we address the problem of categorical information retrieval and ranking of most relevance information while considering the presence of short-text and multilingual languages that arise during such events. Our proposed model is based on the formation of embedding vector with the help of textual and statistical preprocessing, and finally, entire training 2,100,000 vectors were normalized using feed-forward neural network for need and availability tweets. Another important contribution of this paper lies in novel weighted Ranking Key algorithm based on top five general terms to rank the classified tweets in most relevance with classification. Lastly, we test our model on Nepal Earthquake dataset (contains short text and multilingual language tweets) and achieved 6.81% of mean average precision on 5,250,000 unlabeled embedding vectors of disaster relief tweets.