skip to main content


Title: Social Media in Citizen-Led Disaster Response: Rescuer Roles, Coordination Challenges, and Untapped Potential
Widespread disasters can overload official agencies’ capacity to provide assistance, and often citizen-led groups emerge to assist with disaster response. As social media platforms have expanded, emergent rescue groups have many ways to harness network and mobile tools to coordinate actions and help fellow citizens. This study used semi-structured interviews and photo elicitation techniques to better understand how wide-scale rescues occurred during the 2017 Hurricane Harvey flooding in the Greater Houston, Texas USA area. We found that citizens used diverse apps and social media-related platforms during these rescues and that they played one of three roles: rescuer, dispatcher, or information compiler. The key social media coordination challenges these rescuers faced were incomplete feedback loops, unclear prioritization, and communication overload. This work-in-progress paper contributes to the field of crisis and disaster response research by sharing the nuances in how citizens use social media to respond to calls for help from flooding victims.  more » « less
Award ID(s):
1760453
PAR ID:
10076203
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Proceedings of the ... International ISCRAM Conference
ISSN:
2411-3387
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. 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. 
    more » « less
  2. Abstract

    Researchers have established the prominent role digital volunteers play during crises and disasters. From self‐organizing to annotating public data, these volunteers are now a fixture in disaster research. However, we know much less about how these volunteers function, behind the public scene, when using private social media as a disaster unfolds and people need to be rescued. This qualitative study identified the emergent helping roles along with the skillsets and abilities that helped volunteers perform these behind‐the‐scene roles during the Hurricane Harvey flooding in 2017. Using in‐depth interviews along with captured images in private social media, we find these volunteers resembled organizational knowledge workers. We identify nine specific communicating and coordinating actions that these disaster knowledge workers performed. The contributions of these findings centre on implications for disaster response and management.

     
    more » « less
  3. 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. 
    more » « less
  4. 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. 
    more » « less
  5. 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. 
    more » « less