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1. Evaluating Disaster Time-Line from Social Media with Wavelet Analysis

   https://doi.org/10.1109/SMARTCOMP.2018.00072  

   Anam, Amrita; Gangopadhyay, Aryya; Roy, Nirmalya (June 2018, 2018 IEEE International Conference on Smart Computing (SMARTCOMP))

   For over a decade, social media has proved to be a functional and convenient data source in the Internet of things. Social platforms such as Facebook, Twitter, Instagram, and Reddit have their own styles and purposes. Twitter, among them, has become the most popular platform in the research community due to its nature of attracting people to write brief posts about current and unexpected events (e.g., natural disasters). The immense popularity of such sites has opened a new horizon in `social sensing' to manage disaster response. Sensing through social media platforms can be used to track and analyze natural disasters and evaluate the overall response (e.g., resource allocation, relief, cost and damage estimation). In this paper, we propose a two-step methodology: i) wavelet analysis and ii) predictive modeling to track the progression of a disaster aftermath and predict the time-line. We demonstrate that wavelet features can preserve text semantics and predict the total duration for localized small scale disasters. The experimental results and observations on two real data traces (flash flood in Cummins Falls state park and Arizona swimming hole) showcase that the wavelet features can predict disaster time-line with an error lower than 20% with less than 50% of the data when compared to ground truth. 

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2. Information Switching Patterns of Risk Communication in Social Media during Disasters

   https://doi.org/10.1109/TBDATA.2024.3524828  

   Momin, Khondhaker Al; Sadri, Arif Mohaimin; Olofsson, Kristin; Muraleetharan, KK; Gladwin, Hugh (January 2025, IEEE Transactions on Big Data)

   In an era increasingly affected by natural and human-caused disasters, the role of social media in disaster communication has become ever more critical. Despite substantial research on social media use during crises, a significant gap remains in detecting crisis-related misinformation. Detecting deviations in information is fundamental for identifying and curbing the spread of misinformation. This study introduces a novel Information Switching Pattern Model to identify dynamic shifts in perspectives among users who mention each other in crisisrelated narratives on social media. These shifts serve as evidence of crisis misinformation affecting user-mention network interactions. The study utilizes advanced natural language processing, network science, and census data to analyze geotagged tweets related to compound disaster events in Oklahoma in 2022. The impact of misinformation is revealed by distinct engagement patterns among various user types, such as bots, private organizations, non-profits, government agencies, and news media throughout different disaster stages. These patterns show how different disasters influence public sentiment, highlight the heightened vulnerability of mobile home communities, and underscore the importance of education and transportation access in crisis response. Understanding these engagement patterns is crucial for detecting misinformation and leveraging social media as an effective tool for risk communication during disasters 

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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. Training an Emergency-Response Image Classifier on Signal Data

   O'Neal, A.; Rodgers, B.; Segler, J.; Murthy, D.; Lakuduva, N.; Johnson, M.; & Stephens, K. K. (December 2018, Machine learning and applications)

   The increasing popularity of multimedia messages shared through public or private social media spills into diverse information dissemination contexts. To date, public social media has been explored as a potential alert system during natural disasters, but high levels of noise (i.e., non-relevant content) present challenges in both understanding social experiences of a disaster and in facilitating disaster recovery. This study builds on current research by uniquely using social media data, collected in the field through qualitative interviews, to create a supervised machine learning model. Collected data represents rescuers and rescuees during the 2017 Hurricane Harvey. Preliminary findings indicate a 99% accuracy in classifying data between signal and noise for signal-to-noise ratios (SNR) of 1:1, 1:2, 1:4, and 1:8. We also find 99% accuracy in classification between respondent types (volunteer rescuer, official rescuer, and rescuee). We furthermore compare human and machine coded attributes, finding that Google Vision API is a more reliable source of detecting attributes for the training set. 

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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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