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1. Identifying the Context of Hurricane Posts on Twitter using Wavelet Features

   https://doi.org/10.1109/SMARTCOMP.2019.00072  

   Anam, Amrita ; Gangopadhyay, Aryya ; Roy, Nirmalya ( June 2019 , 2019 IEEE International Conference on Smart Computing (SMARTCOMP))

   With the increase of natural disasters all over the world, we are in crucial need of innovative solutions with inexpensive implementations to assist the emergency response systems. Information collected through conventional sources (e.g., incident reports, 911 calls, physical volunteers, etc.) are proving to be insufficient [1]. Responsible organizations are now leaning towards research grounds that explore digital human connectivity and freely available sources of information. U.S. Geological Survey and Federal Emergency Management Agency (FEMA) introduced Critical Lifeline (CLL) s which identifies the most significant areas that require immediate attention in case of natural disasters. These organizations applied crowdsourcing by connecting digital volunteer networks to collect data on the critical lifelines from data sources including social media [3], [4], [5]. In the past couple of years, during some of the deadly hurricanes (e.g., Harvey, IRMA, Maria, Michael, Florence, etc.), people took on different social media platforms like never seen before, in search of help for rescue, shelter, and relief. Their posts reflect crisis updates and their real-time observations on the devastation that they witness. In this paper, we propose a methodology to build and analyze time-frequency features of words on social media to assist the volunteer networks in identifying the context before, during and after a natural disaster and distinguishing contexts connected to the critical lifelines. We employ Continuous Wavelet Transform to help create word features and propose two ways to reduce the dimensions which we use to create word clusters to identify themes of conversations associated with stages of a disaster and these lifelines. We compare two different methodologies of wavelet features and word clusters both qualitatively and quantitatively, to show that wavelet features can identify and separate context without using semantic information as inputs. 

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2. A System Analytics Framework for Detecting Infrastructure-Related Topics in Disasters Using Social Sensing

   Fan, Chao ; Mostafavi, Ali ; Gupta, Aayush ; Zhang, Cheng ( January 2018 , 25th International Workshop on Intelligent Computing in Engineering (EG-ICE))

   The objective of this paper is to propose and test a system analytics framework based on social sensing and text mining to detect topic evolution associated with the performance of infrastructure systems in disasters. Social media, like Twitter, as active channels of communication and information dissemination, provide insights into real-time information and first-hand experience from affected areas in mass emergencies. While the existing studies show the importance of social sensing in improving situational awareness and emergency response in disasters, the use of social sensing for detection and analysis of infrastructure systems and their resilience performance has been rather limited. This limitation is due to the lack of frameworks to model the events and topics (e.g., grid interruption and road closure) evolution associated with infrastructure systems (e.g., power, highway, airport, and oil) in times of disasters. The proposed framework detects infrastructure-related topics of the tweets posted in disasters and their evolutions by integrating searching relevant keywords, text lemmatization, Part-of-Speech (POS) tagging, TF-IDF vectorization, topic modeling by using Latent Dirichlet Allocation (LDA), and K-Means clustering. The application of the proposed framework was demonstrated in a study of infrastructure systems in Houston during Hurricane Harvey. In this case study, more than sixty thousand tweets were retrieved from 150-mile radius in Houston over 39 days. The analysis of topic detection and evolution from user-generated data were conducted, and the clusters of tweets pertaining to certain topics were mapped in networks over time. The results show that the proposed framework enables to summarize topics and track the movement of situations in different disaster phases. The analytics elements of the proposed framework can improve the recognition of infrastructure performance through text-based representation and provide evidence for decision-makers to take actionable measurements. 

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3. Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification

   https://doi.org/10.59297/FWXE4933  

   Zou, Henry Peng ; Caragea, Cornelia ; Zhou, Yue ; and Caragea, Doina ( May 2023 , Proceedings of the 20th International ISCRAM Conference)

   Radianti, Jaziar ; Dokas, Ioannis ; Lalone, Nicolas ; Khazanchi, Deepak (Ed.)

   The shared real-time information about natural disasters on social media platforms like Twitter and Facebook plays a critical role in informing volunteers, emergency managers, and response organizations. However, supervised learning models for monitoring disaster events require large amounts of annotated data, making them unrealistic for real-time use in disaster events. To address this challenge, we present a fine-grained disaster tweet classification model under the semi-supervised, few-shot learning setting where only a small number of annotated data is required. Our model, CrisisMatch, effectively classifies tweets into fine-grained classes of interest using few labeled data and large amounts of unlabeled data, mimicking the early stage of a disaster. Through integrating effective semi-supervised learning ideas and incorporating TextMixUp, CrisisMatch achieves performance improvement on two disaster datasets of 11.2% on average. Further analyses are also provided for the influence of the number of labeled data and out-of-domain results. 

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4. Multimodal Semi-supervised Learning for Disaster Tweet Classification

   Iustin Sirbu, Tiberiu Sosea ( October 2022 , The 29th International Conference on Computational Linguistics (COLING 2022))

   During natural disasters, people often use social media platforms, such as Twitter, to post information about casualties and damage produced by disasters. This information can help relief authorities gain situational awareness in nearly real time, and enable them to quickly distribute resources where most needed. However, annotating data for this purpose can be burdensome, subjective and expensive. In this paper, we investigate how to leverage the copious amounts of unlabeled data generated on social media by disaster eyewitnesses and affected individuals during disaster events. To this end, we propose a semi-supervised learning approach to improve the performance of neural models on several multimodal disaster tweet classification tasks. Our approach shows significant improvements, obtaining up to 7.7% improvements in F-1 in low-data regimes and 1.9% when using the entire training data. We make our code and data publicly available at https://github.com/iustinsirbu13/multimodal-ssl-for-disaster-tweet-classification. 

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5. Evaluating the performance of Deep learning methods for hurricane-related image classification.

   Johnson, M. ; Murthy, D. ; Robertson, B. W. ; Smith, W. R. ; & Stephens, K. K. ( January 2019 , Proceedings of the ... International ISCRAM Conference)

   Global social media use during natural disasters has been well documented (Murthy et al., 2017). In the U.S., public social media platforms are often a primary venue for those affected by disasters . Some disaster victims believe first responders will see their public posts and that the 9-1-1 telephone system becomes overloaded during crises. Moreover, some feel that the accuracy and utility of information on social media is likely higher than traditional media sources . However, sifting through content during a disaster is often difficult due to the high volume of ‘non-relevant’ content. In addition, text is studied more than images posted on Twitter, leaving a potential gap in understanding disaster experiences. Images posted on social media during disasters have a high level of complexity (Murthy et al., 2016). Our study responds to O’Neal et al.’s (2017) call-to-action that social media images posted during disasters should be studied using machine learning. 

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