16 research outputs found
NARMADA: Need and Available Resource Managing Assistant for Disasters and Adversities
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.Comment: ACL 2020 Workshop on Natural Language Processing for Social Media
(SocialNLP
An Empirical Methodology for Detecting and Prioritizing Needs during Crisis Events
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
OntoDSumm : Ontology based Tweet Summarization for Disaster Events
The huge popularity of social media platforms like Twitter attracts a large
fraction of users to share real-time information and short situational messages
during disasters. A summary of these tweets is required by the government
organizations, agencies, and volunteers for efficient and quick disaster
response. However, the huge influx of tweets makes it difficult to manually get
a precise overview of ongoing events. To handle this challenge, several tweet
summarization approaches have been proposed. In most of the existing
literature, tweet summarization is broken into a two-step process where in the
first step, it categorizes tweets, and in the second step, it chooses
representative tweets from each category. There are both supervised as well as
unsupervised approaches found in literature to solve the problem of first step.
Supervised approaches requires huge amount of labelled data which incurs cost
as well as time. On the other hand, unsupervised approaches could not clusters
tweet properly due to the overlapping keywords, vocabulary size, lack of
understanding of semantic meaning etc. While, for the second step of
summarization, existing approaches applied different ranking methods where
those ranking methods are very generic which fail to compute proper importance
of a tweet respect to a disaster. Both the problems can be handled far better
with proper domain knowledge. In this paper, we exploited already existing
domain knowledge by the means of ontology in both the steps and proposed a
novel disaster summarization method OntoDSumm. We evaluate this proposed method
with 4 state-of-the-art methods using 10 disaster datasets. Evaluation results
reveal that OntoDSumm outperforms existing methods by approximately 2-66% in
terms of ROUGE-1 F1 score
PORTRAIT: a hybrid aPproach tO cReate extractive ground-TRuth summAry for dIsaster evenT
Disaster summarization approaches provide an overview of the important
information posted during disaster events on social media platforms, such as,
Twitter. However, the type of information posted significantly varies across
disasters depending on several factors like the location, type, severity, etc.
Verification of the effectiveness of disaster summarization approaches still
suffer due to the lack of availability of good spectrum of datasets along with
the ground-truth summary. Existing approaches for ground-truth summary
generation (ground-truth for extractive summarization) relies on the wisdom and
intuition of the annotators. Annotators are provided with a complete set of
input tweets from which a subset of tweets is selected by the annotators for
the summary. This process requires immense human effort and significant time.
Additionally, this intuition-based selection of the tweets might lead to a high
variance in summaries generated across annotators. Therefore, to handle these
challenges, we propose a hybrid (semi-automated) approach (PORTRAIT) where we
partly automate the ground-truth summary generation procedure. This approach
reduces the effort and time of the annotators while ensuring the quality of the
created ground-truth summary. We validate the effectiveness of PORTRAIT on 5
disaster events through quantitative and qualitative comparisons of
ground-truth summaries generated by existing intuitive approaches, a
semi-automated approach, and PORTRAIT. We prepare and release the ground-truth
summaries for 5 disaster events which consist of both natural and man-made
disaster events belonging to 4 different countries. Finally, we provide a study
about the performance of various state-of-the-art summarization approaches on
the ground-truth summaries generated by PORTRAIT using ROUGE-N F1-scores
Analyzing the Needs of Ukrainian Refugees on Telegram in Real-Time: A Machine Learning Approach
The humanitarian crisis resulting from the Russian invasion of Ukraine has led to millions of displaced individuals across Europe. Addressing the evolving needs of these refugees is crucial for hosting countries and humanitarian organizations. This study leverages social media analytics to supplement traditional surveys, providing real-time insights into refugee needs by analyzing over two million messages from Telegram, a vital platform for Ukrainian refugees in Germany. We employ Natural Language Processing techniques, including language identification, sentiment analysis, and topic modeling, to identify well-defined topic clusters such as housing, financial and legal assistance, language courses, job market access, and medical needs. Our findings also reveal changes in topic occurrence and nature over time. To support practitioners, we introduce an interactive web-based dashboard for continuous analysis of refugee needs
Multi-source Multimodal Data and Deep Learning for Disaster Response: A Systematic Review.
Mechanisms for sharing information in a disaster situation have drastically changed due to new technological innovations throughout the world. The use of social media applications and collaborative technologies for information sharing have become increasingly popular. With these advancements, the amount of data collected increases daily in different modalities, such as text, audio, video, and images. However, to date, practical Disaster Response (DR) activities are mostly depended on textual information, such as situation reports and email content, and the benefit of other media is often not realised. Deep Learning (DL) algorithms have recently demonstrated promising results in extracting knowledge from multiple modalities of data, but the use of DL approaches for DR tasks has thus far mostly been pursued in an academic context. This paper conducts a systematic review of 83 articles to identify the successes, current and future challenges, and opportunities in using DL for DR tasks. Our analysis is centred around the components of learning, a set of aspects that govern the application of Machine learning (ML) for a given problem domain. A flowchart and guidance for future research are developed as an outcome of the analysis to ensure the benefits of DL for DR activities are utilized.Publishe