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Towards an Interpretable Approach to Classify and Summarize Crisis Events from Microblogs

Thi Huyen Nguyen, Koustav Rudra
2022 Proceedings of the ACM Web Conference 2022  
This paper proposes an interpretable classification-summarization framework that first classifies tweets into different disaster-related categories and then summarizes those tweets.  ...  Unlike existing work, our classification model can provide explanations or rationales for its decisions.  ...  CONCLUSION This paper presents an interpretable classification and summarization framework for disaster events on Twitter.  ... 
doi:10.1145/3485447.3512259 fatcat:oevt252yhfd7fbqh6sw32zac7m

Geographic Situational Awareness: Mining Tweets for Disaster Preparedness, Emergency Response, Impact, and Recovery

Qunying Huang, Yu Xiao
2015 ISPRS International Journal of Geo-Information  
Social media data have emerged as a new source for detecting and monitoring disaster events.  ...  during a natural disaster and referencing the findings from the relevant literature and official government procedures involving different disaster stages.  ...  Acknowledgement We would like to thank Caitlin McKown for the help and support during this research. Work performed under this project has been funded by the UW-Madison WARF grant # PRJ93XF.  ... 
doi:10.3390/ijgi4031549 fatcat:jthenqdbfrh4ril2ocynx5kxzm

Natural Disasters Detection in Social Media and Satellite imagery: a survey [article]

Naina Said, Kashif Ahmad, Michael Regular, Konstantin Pogorelov, Laiq Hassan, Nasir Ahmad, Nicola Conci
2019 arXiv   pre-print
Furthermore, we also review benchmarking datasets available for the evaluation of disaster detection frameworks.  ...  Satellite imagery has also been widely explored for disasters analysis.  ...  For the training stage the authors adopt a modified VGGNet [112] model.  ... 
arXiv:1901.04277v1 fatcat:5zidbp3owbe6ld33pt4j3aijtq

A novel framework for assessing the criticality of retrieved information

Ashwani Varshney, Yatin Kapoor, Vaishali Chawla, Vibha Gaur
2022 International Journal of Computing and Digital Systems  
Taking advantage of Deep Learning (DL) and Natural Language Processing (NLP) techniques, this paper proposes a novel framework for retrieving critical information from Twitter to manage emergencies effectively  ...  The proposed framework classifies the tweets into relevant and irrelevant classes using Bidirectional Encoder Representations from Transformers (BERT).  ...  Acknowledgment We thank the anonymous annotators for annotating the dataset utilized in the study. Moreover, we thank the anonymous reviewers for their valuable suggestions.  ... 
doi:10.12785/ijcds/1101100 fatcat:wz4uc236vret3c5i7otmmmmtcu

DCBRTS: A Classification-Summarization Approach for Evolving Tweet Streams in Multiobjective Optimization Framework

Diksha Bansal, Naveen Saini, Sriparna Saha
2021 IEEE Access  
In the first stage, tweet category is identified as either situational or non-situational using a classification framework.  ...  CONCLUSION The current article presents a novel framework for classification followed by summarization to handle the continuous tweet streams posted during disaster events.  ... 
doi:10.1109/access.2021.3120112 fatcat:nofzfl4gwjckbfllgm6szdwfyu

Neural Networks and Support Vector Machine based Approach for Classifying Tweets by Information Types at TREC 2018 Incident Streams Task

Abu Nowshed Chy, Umme Aymun Siddiqua, Masaki Aono
2018 Text Retrieval Conference  
We consider a rich set of hand-crafted features to train our multi-class SVM classifier, whereas a pre-trained word2vec model is used for the DNN based classifiers.  ...  Moreover, we introduce a set of rules based on the language of tweets, exploiting indicator terms, and WH-orientation of tweets for our rule-based classifier.  ...  Acknowledgments This research was supported by MEXT KAKENHI, Grant-in-Aid for Scientific Research (B), Grant Number 17H01746.  ... 
dblp:conf/trec/ChySA18 fatcat:s3pa4f6ncvgrvlnv52nm2qmn7u

Time-Critical Geolocation for Social Good [chapter]

Reem Suwaileh
2020 Lecture Notes in Computer Science  
I specifically aim to study the Location Mention Prediction problem in which the system has to extract location mentions in tweets and pin them on the map.  ...  Nevertheless, due to several challenges, the current fully-automated processing methods are not yet mature enough for deployment in real scenarios.  ...  This work was made possible by GSRA grant# GSRA5-1-0527-18082 from the Qatar National Research Fund (a member of Qatar Foundation).  ... 
doi:10.1007/978-3-030-45442-5_82 fatcat:lrmzieeiwbgadlnidy6y3kyl4u

Automated machine learning approaches for emergency response and coordination via social media in the aftermath of a disaster: A review

Lokabhiram Dwarakanath, Amirrudin Kamsin, Rasheed Abubakar Rasheed, Anitha Anandhan, Liyana Shuib
2021 IEEE Access  
This review would help researchers in choosing further research topics pertaining to automated approaches for actionable information classification and disaster coordination and would help the emergency  ...  Regardless of the kind of disaster event, whether it is a hurricane, a flood, an earthquake or a man-made disaster event like a riot or a terrorist attack, social media platforms like Facebook, Twitter  ...  The authors published a large dataset of 524 million multi-lingual tweets from various countries pertaining to a COVID-19 disaster event.  ... 
doi:10.1109/access.2021.3074819 fatcat:2ebym34ewjcgzn7im7gaac7udy

Sympathy Detection in Disaster Twitter Data

Yingjie Li, Seoyeon Park, Cornelia Caragea, Doina Caragea, Andrea H. Tapia
2019 International Conference on Information Systems for Crisis Response and Management  
Specifically, we propose a refined word embedding technique in terms of various pre-trained word vector models and show great performance of CNNs that use these refined embeddings in the sympathy tweet  ...  classification task.  ...  We also wish to thank our anonymous reviewers for their constructive comments.  ... 
dblp:conf/iscram/LiPCCT19 fatcat:esx6sgbnsrhl3gpmnb6bz6xlg4

Tackling the Challenges of Situational Awareness Extraction in Twitter with an Adaptive Approach

Haji Mohammad Saleem, Faiyaz Al Zamal, Derek Ruths
2015 Procedia Engineering  
Furthermore, we introduce a novel data model based on a three-label classification scheme to describe the composition of the data-stream.  ...  Despite interest in the development of extraction systems for such information, little effort has been put towards systemic methods for obtaining all posts pertaining to a disaster from the live Twitter  ...  This is true for disaster and non-disaster tweets alike [8] .  ... 
doi:10.1016/j.proeng.2015.06.085 fatcat:skzll667hzcahipljpztaqh6xu

Exploit Social Relations in Sentiment Analysis of Social Media Content for Disaster Management

Pratik Shivarkar, Wei Wei
2018 Americas Conference on Information Systems  
The proposed study extends previous work substantially by looking at a larger set of social relations and focusing on different communication goals at each stage of disaster management.  ...  The study can help formulate a better understanding of how opinions are formed and propagated during disasters, thus allow stakeholders to strategize for better communication.  ...  different stage of a disaster.  ... 
dblp:conf/amcis/ShivarkarW18 fatcat:ahb5a5e6izfmloygd4c5jtmwkm

Deep Learning Approaches for Multi-Label Incidents Classification from Twitter Textual Information

Sherly Rosa Anggraeni, Narandha Arya Ranggianto, Imam Ghozali, Chastine Fatichah, Diana Purwitasari
2022 Journal of Information Systems Engineering and Business Intelligence  
Results: CNN paired with NeuroNER yield the best results for multi-label classification compared to CLSTM and RCNN.  ...  Conclusion: CNN was proven to be more effective with an average precision value of 88.54% for multi-label incidents classification.  ...  Funding: research received no specific grant from any funding agency. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.20473/jisebi.8.1.31-41 fatcat:3ln4cp3lrfcc5o227iebj6g2nm

Use of Social Media Data in Disaster Management: A Survey

Jedsada Phengsuwan, Tejal Shah, Nipun Balan Thekkummal, Zhenyu Wen, Rui Sun, Divya Pullarkatt, Hemalatha Thirugnanam, Maneesha Vinodini Ramesh, Graham Morgan, Philip James, Rajiv Ranjan
2021 Future Internet  
To the best of our knowledge, there is no published literature on social media data management and analysis that identifies the research problems and provides a research taxonomy for the classification  ...  In this paper, we provide a survey of how social media data contribute to disaster management and the methodologies for social media data management and analysis in disaster management.  ...  Used a support vector machine (SVM) for the tweet classification and a probabilistic model for location estimation.  ... 
doi:10.3390/fi13020046 fatcat:2p5xerp3cngkhb2myi7vmwe4pm

Analytics and Evolving Landscape of Machine Learning for Emergency Response [chapter]

Rajendra Akerkar Minsung Hong
2019 Zenodo  
Based on the literature review, we observe a trend to move from narrow in scope, problem-specific applications of data mining and machine learning to solutions that address a wider spectrum of problems  ...  The purpose of this chapter is to discuss a hybrid crowdsourcing and real-time ma- chine learning approaches to rapidly process large volumes of data for emergency response in a time-sensitive manner.We  ...  Acknowledgements The work is funded from the Research Council of Norway (RCN) and the Norwegian Centre for International Cooperation in Education (SiU) grant through INT-PART programme.  ... 
doi:10.5281/zenodo.5106014 fatcat:ui7kmryflbaljkim4u2ync2dqi

Designing Multimodal Interactive Dashboard of Disaster Management Systems

Abeer AlAbdulaali, Amna Asif, Shaheen Khatoon, Majed Alshamari
2022 Sensors  
In the aftermath of a disaster, a society's overall growth, resources, and economy are greatly affected as they cause damages from minor to huge proportions.  ...  The proposed interactive multimodal dashboards complement the existing techniques of collecting textual, image, audio, and video emergency information and their classifications for usable presentation.  ...  Acknowledgments: The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia.  ... 
doi:10.3390/s22114292 pmid:35684913 pmcid:PMC9185355 fatcat:laavygep6jbyhcnobqu3caikqa
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