NARMADA : Need and Available Resource Managing Assistant for Disasters and Adversities (2020)

Abstract: Although a lot of research has been done on utilising Online Social Media during disasters, there exists no system for a specific task that is critical in a post-disaster scenario – identifying resource-needs and resource-availabilities in the disaster-affected region, coupled with their subsequent matching. To this end, we present NARMADA, a semi-automated platform which leverages the crowd-sourced information from social media posts for assisting post-disaster relief coordination efforts. The system employs Natural Language Processing and Information Retrieval techniques for identifying resource-needs and resource-availabilities from microblogs, extracting resources from the posts, and also matching the needs to suitable availabilities. The system is thus capable of facilitating the judicious management of resources during post-disaster relief operations.

## introduction in recent years, microblogging sites like twitter and weibo have played a pivotal role in gathering situational information during disasters or emergency scenarios such as earthquakes, epidemic outbreaks, floods, hurricanes, and so on @xcite @xcite . specifically, there are two types of information which are considered useful (or 'actionable') by rescue workers for assisting post-disaster relief operations. 1 these include (i) resource needs that talk about the requirement of a specific resource (such as food, water, shelter) and (ii) resource availabilities that talk about the availability of a specific resource in the region. some examples of tweets that inform *equal contribution 1 we discussed with relief workers from 'doctors for you' ( http://doctorsforyou.org/ ) and spade ( http: //www.spadeindia.org/ ). ## related work there has been a lot of recent work on utilising online social media (osm) to facilitate post-disaster relief operations -see @xcite @xcite for some recent surveys on this topic. for instance, there have been works on classifying situational and non-situational information @xcite , location inferencing from social media posts during disasters @xcite @xcite @xcite , early detection of rumours from social media posts @xcite , emergency information diffusion on social media during crises @xcite , event detection @xcite , extraction of event-specific informative tweets during disaster @xcite and so on. tweets specific to particular disasters have been studied in @xcite , along with their categorisation. certain other works have focused on the classification of such tweets by determining the probability of them being re-shared in twitter @xcite . a comparison of various learning-based methods has also been recently conducted in @xcite . automated retrieval of needs and availabilities have been attempted by employing regular expressions @xcite , pattern-matching techniques @xcite , language models @xcite , and neural ir methods such as word and character embeddings @xcite . likewise, there has been prior research on the automated matching of the needs and availabilities using tf-idf similarity @xcite and our prior works @xcite that used word-embeddings for the task. however, no prior work has attempted end-to-end identification and matching of needs and availabilities, which we attempt in this work. some information systems have also been implemented for disaster situations such as aidr @xcite and @xcite which employs crowd-sourced information using social media to assist disaster operations. to our knowledge, none of the existing systems have attempted the specific tasks in this work -identification and matching of resource-needs and resource-availabilities. ## dataset we reuse the dataset made available by our prior works @xcite @xcite which comprises tweets posted during two disaster events i.e. (i) the earthquake in nepal in april, 2015 2 , and (ii) the earthquake in central italy in august, 2016 foot_1 . henceforth, we refer to the scenarios as nepal-quake and italy-quake. the tweets were collected using the twitter search api foot_2 with the queries 'nepal quake' and 'italy quake'. the dataset consists of only english tweets since it was observed that most tweets are posted in english to enable rapid communication between international agencies and the local population. removing duplicates and near-duplicates yielded a corpus of 50,068 tweets for nepal-quake and 70,487 tweets for italy-quake. however, the number of tweets that inform about needs and availabilities is very low -there are 499 and 1333 need and availability tweets for the nepal-quake dataset. likewise, the italy-quake had only 177 needs and 233 availabilities (see @xcite for more details). ## methodology in this section, we describe the methodologies that are incorporated within the narmada system. the overarching goal of the system is to facilitate post-disaster relief coordination efforts using the vast information available on social media. to that end, it performs three essential tasks -(i) identifying needs and availabilities, (ii) extracting actionable information from the need and availability tweets, and (iii) matching appropriate needs and availabilities. narmada is designed to execute each of the above three tasks in an automated fashion. we elaborate on the specific methodology involved for each of these sub-tasks in the ensuing subsection. however, prior to each of these tasks, we perform pre-processing on the tweet text as follows. pre-processing tweets: we employed standard pre-processing techniques on the tweet text to remove urls (but not email ids), mentions, characters like brackets, 'rt', and other non-ascii characters like #, &, ellipses and unicode characters corresponding to emojis. we also segmented camelcase words and joint alphanumeric terms like 'nepal2015' into distinct terms @xcite . however, we did not perform case-folding or stemming on the tweet-text to enable subsequent detection of proper nouns (explained below). ## system architecture the high-level system architecture for narmada is shown in figure 1 . the system can be accessed from https://osm-dm-kgp.github.io/ narmada/ , where further details and a demonstration video are also provided. narmada is designed and built for the web, thus not restricting it to any particular operating system or browser type, allowing cross-platform (desktop/mobile) functionality. ## discussion narmada intends to assist in crossing the initial barrier in identifying and matching needs and availabilities from social media during the occurrence of a disaster. in practice, it becomes necessary for other service providers to be triggered in order to make sure that the needs are addressed, by proper collection, transportation and provisioning of the matched resources deemed to be available. for instance, the needs and availabilities could be marked on a map, with each type of resource being represented with a different symbol, making it easy to physically locate them. local volunteers might be provided with a mobile app to help them find nearby needs and availabilities. misinformation in twitter is common @xcite . the volunteers would also need a facility to confirm that the posted needs and availabilities are indeed genuine, concerning various parameters such as quantity (since at times of disasters, needs may be exaggerated). ## conclusion and future work we proposed a system narmada for resource management during a disaster situation. though the system is developed to work across posts from various social media platform, this research focused on data from twitter. the real-time nature and easy access to large volumes of information provided by twitter have made it a lucrative choice for disaster analytics. currently, the system allows all users to perform any action on the system. one future task would be to implement a login system that would allow different access-levels to different users. for instance, a visitor would be able to only view and query information, a volunteer would be able to add new resources, mark a need as matched, etc., while a system administrator would have rights to undo all actions of all users, etc. the current system does not allow multiple volunteers to communicate within the platform over a resource, which we wish to incorporate in the future. we also plan to incorporate support for vernacular languages, provided the requisite tools are available.