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1. Citizens Communicating Health Information: Urging Others in their Community to Seek Help During a Flood

   Stephens, K. K.; Li, J.; Robertson, B. W.; Smith, W. R. (May 2018, Proceedings of the ... International ISCRAM Conference)

   When wide-scale flooding occurs in a community not accustomed to floods, health concerns emerge. While official organizations tasked with communicating emerging health information exist, the proliferation of social media makes it possible for average citizens to participate in this conversation. This study used a combination of semi-structured interviews and photo elicitation techniques to explore how citizens used private social media sites to share health information. We found two main categories of health concerns: existing medical conditions and water-created. We further identified six themes that describe the common approaches average citizens used to share health information: Narrating a personal experience, presenting it as a Public Service Announcement, downplaying the contribution, bringing a credible source into the conversation, including external links and sources, and using humor. Together, these findings suggest that citizens need health information during a flood disaster, and when they do not have it available from official sources, they use their private social media to tap into a shared community identity and carefully help one another. 

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2. Improve disaster communication in hyperlocal online and offline communities using social media data: A case study of the 2015 Nepal earthquake.

   Dozier, J.; McFarland, K; Ghanipoor Machiani, S; Nara, A; Yang, X.; Tsou, M.H. (January 2019, Proceedings in 2019 Annual Meeting of Transportation Research Board (TRB), January 13–17, Washington D.C.)

   This article seeks to go beyond traditional GIS methods used in creating maps for disaster response that commonly look at the disaster extent. Instead, a slightly different approach is taken using social media data collected from Twitter to explore how people communicate during disaster events, how online communities form and evolve, and how communication methods can improve. This study collected the Twitter data during the 2015 Nepal earthquake disaster and applied a spatiotemporal analysis to find any patterns that show shadows or gaps in communication channels in local communities’ communication. Linkages in social media can be used to understand how people communicate, how quickly they diffuse information, and how social networks form online during disasters. These can improve communication throughout disaster phases. This study offers a deeper understanding of the kinds of spatiotemporal patterns and spatial social networks that can be observed during disaster events. The need for better communication during disaster events is imperative for better disaster management, increasing community resilience, and saving lives. 

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3. Online Delivery of Social Media Posts to Appropriate First Responders for Disaster Response

   https://doi.org/10.1145/3427477.3429272  

   Mittal, Viyom; Jahanian, Mohammad; Ramakrishnan, K. K. (December 2020, In 3rd International Workshop on Emergency Response Technologies and Services in Adjunct Proceedings of the 2021 International Conference on Distributed Computing and Networking (ICDCN ’21))

   null (Ed.)

   Delivering the right information to the right people in a timely manner can greatly improve outcomes and save lives in emergency response. A communication framework that flexibly and efficiently brings victims, volunteers, and first responders together for timely assistance can be very helpful. With the burden of more frequent and intense disaster situations and first responder resources stretched thin, people increasingly depend on social media for communicating vital information. This paper proposes ONSIDE, a framework for coordination of disaster response leveraging social media, integrating it with Information-Centric dissemination for timely and relevant dissemination. We use a graph-based pub/sub namespace that captures the complex hierarchy of the incident management roles. Regular citizens and volunteers using social media may not know of or have access to the full namespace. Thus, we utilize a social media engine (SME) to identify disaster-related social media posts and then automatically map them to the right name(s) in near-real-time. Using NLP and classification techniques, we direct the posts to appropriate first responder(s) that can help with the posted issue. A major challenge for classifying social media in real-time is the labeling effort for model training. Furthermore, as disasters hits, there may be not enough data points available for labeling, and there may be concept drift in the content of the posts over time. To address these issues, our SME employs stream-based active learning methods, adapting as social media posts come in. Preliminary evaluation results show the proposed solution can be effective. 

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4. VictimFinder: Harvesting rescue requests in disaster response from social media with BERT

   https://doi.org/10.1016/j.compenvurbsys.2022.101824  

   Zhou, B.; Zou, L.; Mostafavi, A.; Lin, B.; Yang, M.; Gharaibeh, N.; Cai, H.; Abedin, J.; Mandal, D. (July 2022, Computers environment and urban systems)

   Social media platforms are playing increasingly critical roles in disaster response and rescue operations. During emergencies, users can post rescue requests along with their addresses on social media, while volunteers can search for those messages and send help. However, efficiently leveraging social media in rescue operations remains challenging because of the lack of tools to identify rescue request messages on social media automatically and rapidly. Analyzing social media data, such as Twitter data, relies heavily on Natural Language Processing (NLP) algorithms to extract information from texts. The introduction of bidirectional transformers models, such as the Bidirectional Encoder Representations from Transformers (BERT) model, has significantly outperformed previous NLP models in numerous text analysis tasks, providing new opportunities to precisely understand and classify social media data for diverse applications. This study developed and compared ten VictimFinder models for identifying rescue request tweets, three based on milestone NLP algorithms and seven BERT-based. A total of 3191 manually labeled disaster-related tweets posted during 2017 Hurricane Harvey were used as the training and testing datasets. We evaluated the performance of each model by classification accuracy, computation cost, and model stability. Experiment results show that all BERT-based models have significantly increased the accuracy of categorizing rescue-related tweets. The best model for identifying rescue request tweets is a customized BERT-based model with a Convolutional Neural Network (CNN) classifier. Its F1-score is 0.919, which outperforms the baseline model by 10.6%. The developed models can promote social media use for rescue operations in future disaster events. 

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5. 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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