Abstract: Social media can inform response agencies during disasters to help affected people. However, filtering informative messages from social media content is challenging due to the ungrammatical text, out-of-vocabulary words, etc., that limit the context interpretation of messages. Further, there has been limited exploration of the challenge of code-mixing (using words from another language in a given text of one language) in user-generated content during disasters. Hence, we proposed a new code-mixed dataset of tweets related to the 2017 Iran-Iraq Earthquake and annotated them based on their informativeness characteristics. Additionally, we have evaluated the performance of state-of-the-art pre-trained language models: mBERT, RoBERTa, and XLM-R, on the proposed dataset. The results show that mBERT (with F1 score of 72%) overweighs the other models in classifying informative code-mixed messages. Moreover, we analyzed some patterns of exploiting code-mixing by users, which can help future works in developing these models.

Abstract: This research-in-progress paper reports on the use of microblogging as a communication and information sharing resource during a recent violent crisis. The goal of the larger research effort is to investigate the role that microblogging plays in crisis communication during violent events. The shooting of four police officers and the subsequent 48-hour search for the suspect that took place in the Seattle-Tacoma area of Washington in late November 2009 is used as a case study. A stream of over 6,000 publically available messages on Twitter, a popular microblogging site, was collected and individual messages were categorized as information, opinion, technology, emotion, and action-related. The coding and statistical analyses of the messages suggest that citizens use microblogging as one method to organize and disseminate crisis-related information. Additional research is in progress to analyze the types of information transmitted, the sources of the information, and the temporal trends of information shared.

Abstract: In a networked world, we are increasingly inundated with information from online data streams especially from the social web. Curation has increasingly become the buzzword for managing this problem of information overload in the digital age. However, the applications and interpretations of curation by social web users are varied and often stray away from traditional curator roles. I present seven curatorial activities (i.e. collecting, organizing, preserving, filtering, crafting a story, displaying, and facilitating discussions) based on the analysis of 100 web artifacts. I introduce the concept, socially-distributed curation, to emphasize the distributed nature of this curatorial process emerging from the social web. Lastly, I present seven case studies to illustrate preliminary examples of curated crisis content for four crises. These findings are to inform future designs and developments of crisis management tools that could benefit from curated crisis content.

Abstract: In this paper, we propose a low-tech solution for use by microbloggers that could enhance their ability to rapidly produce parsable, crisis-relevant information in mass emergencies. We build upon existing research on the use of social media during mass emergencies and disasters. Our proposed intervention aims to leverage the affordances of mobile microblogging and the drive to support citizen reporting within current behavioral Twitter-based microblogging practice. We introduce a prescriptive, tweet-based syntax that could increase the utility of information generated during emergencies by gently reshaping current behavioral practice. This offering is grounded in an understanding of current trends in norm evolution of Twitter use, an evolution that has progressed quickly but appears to be stabilizing around specific textual conventions.

Abstract: This paper presents findings from an interdisciplinary research effort studying community resilience in Boulder, Colorado. Boulder is a progressive region with a history of environmental leadership. The area is currently in the process of recovering from major flooding and has launched several new initiatives related to building long-term resilience to natural disasters and other stressors. In our research, we consider the stakeholders involved in building local resilience as well as the different and often contradictory framings of the concept. This study takes a phenomenological and inductive approach to understanding resilience. In contrast to more reductionist frameworks that are frequently offered, we argue that this allows for greater understanding of the polyvocal and emergent qualities of resilience.

Abstract: Advancements in information and communication technology (ICT) offer new possibilities when dealing with crisis situations. In this paper we present the design for a crowd tasking tool (CTT) that is currently under development. We describe how the tool can assist disaster relief coordinators during a crisis by selectively distributing tasks to a crowd of volunteers. We also compare the CTT with an already existing ICT based solution for supporting volunteerism during crisis. The differences between these two tools are addressed and the implications for volunteerism are discussed. The paper concludes with an outlook on future work emphasizing a form of volunteer involvement that offers potential for gathering information that is more relevant and easier to digest for decision-making than information provided solely by self-organised volunteers through social media.

Abstract: Microblogging platforms have been increasingly used in crisis, facilitating more participatory communication between official response channels and affected communities. Despite the potential benefits, research has shown that disaster response organizations could not effectively utilize social media data due to data deluge (Knox 2022). To better understand the information needed for disaster response, we turn to the National Incident Management System Guidance for public information officers (PIOs) (NIMS Basic Guidance for PIOs 2020), the primary spokesperson for emergency management organizations. The guidance indicates that PIOs use social media for two major purposes, supporting their operational needs and gauging public perception of risk and incident response. To support the operational needs, the crisis informatics literature has heavily focused on information types supporting situational awareness, including serviceable, eyewitness or actionable information. However, the information representing public perception, such as people’s cognitive and perceptual processes in response to incidents, has been less addressed at scale. To bridge the gap between quantitative study in crisis informatics and information representing cognitive and perceptual processes and better support the task of PIOs, I focus on the study of people’s cognitive and perceptual processes on social media for my research. Cognitive and perceptual processes refer to the way that people pay attention to or process environmental inputs, including the mental activities of acquisition, processing or evaluation of environmental cues, social cues, and warnings. These processes reveal people’s perception of- and decision-making in response to potential threats. With this focus, I seek to answer the following research question: How could people’s cognitive and perceptual processes be inferred from their social media activities in crisis to benefit stakeholders in incident response? My interest in tracing this overall theme through a varied range of sub-tasks produces three more specific research questions: RQ1. How can information exposure and attention be operationalized to highlight cognitive and perceptual processes? RQ2. How do people’s perception of risk communications from stakeholders vary in crisis? RQ3. How could a principled and scalable pipeline be designed to identify people’s cognitive and affective perceptions on Twitter? I took cues from the Protective Action Decision Model (Lindell and Perry 2012) and leveraged baselines in the literature to address these research questions. To address the first research question, I proposed a metric that conceptualized and operationalized the predecision process. The proposed metric was incorporated into a pipeline and applied to two real-word events to recommend messages that represent the shift of collective attention of those locally affected with a specialized focus on cognitive and perceptual processes. To address the second research question, I went beyond the perception of risks to include perceptions of risk communications by stakeholders. I performed an empirical study of the relation between risk communications by stakeholders and different kinds of public perceptions (Lindell and Perry 2012). To address the third research question, I proposed a future work to provide benchmark coding schemes, datasets and models to quantitatively identify information representing cognitive and perceptual processes. I will leverage existing benchmark datasets in the literature (Olteanu et al. 2014; Imran et al. 2016; Alam et al. 2018; Zahra et al. 2020; Rudra et al. 2017; Mazloom et al. 2018; Purohit et al. 2018) and coding schemes in qualitative studies (Trumbo et al. 2016; Demuth et al. 2018) and create benchmark classification models.

Abstract: Developments in information and communication technology (ICT) have adjusted the opportunities for spatial and temporal representations of data, possibly permitting the simultaneous visualization of how different regions and populations are affected during large-scale emergencies and crises. We surveyed 13 crisis-related mashups to derive some high-level design directions to guide the design and testing of next generation crisis support tools. The current web mashups offer a new way of looking at how crises are spatiotemporally ordered. However, since all technology is constrained by limitations of design choice, examining the limits and possibilities of what current design choices afford can inform attributes of what next generation crisis support tools would require.

Abstract: Practice and research collaborations in the disaster domain have the potential to improve emergency management practices while also advancing disaster science theory. However, they also pose challenges as practitioners and researchers each have their own culture, history, values, incentives, and processes that do not always facilitate collaboration. In this paper, we reflect on a 6-month practice and research collaboration, where researchers and practitioners worked together to craft a social media monitoring system for emergency managers in response to the COVID-19 pandemic. The challenges we encountered in this project fall into two broad categories, job-related and timescale challenges. Using prior research on team science as a guide, we discuss several challenges we encountered in these two categories and show how our team sought to overcome them. We conclude with a set of best practices for improving practice and research collaborations.

Abstract: This paper depicts the real-time sensemaking of experts as they worked to combat the first emerging disease of the 21st century: Severe Acute Respiratory Syndrome (SARS). Newspaper data was analysed from the 2003 SARS crisis, with a Canadian perspective, to follow the process of solving the puzzle of this emerging disease. Retrospective sensemaking is a process that is triggered by the unexpected, which leads to actors gathering information (taking action) in order to consider possible interpretations for the unexpected event. Disease outbreaks serve as sensemaking triggers, and actors engage in retrospective sensemaking to find out the factors involved in how the outbreak happened. Prospective sensemaking (future-oriented) is employed when actors work together to plan how to combat the disease. The newspaper data demonstrate that retrospective and prospective sensemaking are tethered: to make plans to combat a disease, actors first require a collectively agreed upon understanding from which they can generate possibilities for a crisis response. This paper contributes to the field by providing concepts for long-duration crisis sensemaking, as the bulk of organisational research focuses on acute crises such as wildfires, or earthquakes.

Abstract: We report on the use of a team of trusted digital volunteers during the 2011 Shadow Lake Fire that occurred in the US Pacific Northwest to extend the social media capacity of a Type I incident management team. In this case study, we outline the tools and processes used by this virtual team to coordinate their activities, monitor social media communication and to establish communications with the public around the event. Finally, we discuss the potential merits and limitations of implementing a team of trusted volunteers and explore how this idea could be incorporated into emergency management organizations. © 2012 ISCRAM.

Abstract: This paper summarizes the final year of the four-year Text REtrieval Conference Incident Streams track (TREC-IS), which has produced a large dataset comprising 136,263 annotated tweets, spanning 98 crisis events. Goals of this final year were twofold: 1) to add new categories for assessing messages, with a focus on characterizing the audience, author, and images associated with these messages, and 2) to enlarge the TREC-IS dataset with new events, with an emphasis of deeper pools for sampling. Beyond these two goals, TREC-IS has nearly doubled the number of annotated messages per event for the 26 crises introduced in 2021 and has released a new parallel dataset of 312,546 images associated with crisis content – with 7,297 tweets having annotations about their embedded images. Our analyses of this new crisis data yields new insights about the context of a tweet; e.g., messages intended for a local audience and those that contain images of weather forecasts and infographics have higher than average assessments of priority but are relatively rare. Tweets containing images, however, have higher perceived priorities than tweets without images. Moving to deeper pools, while tending to lower classification performance, also does not generally impact performance rankings or alter distributions of information-types. We end this paper with a discussion of these datasets, analyses, their implications, and how they contribute both new data and insights to the broader crisis informatics community.

Abstract: Over the years, with the increase in social media engagement, there has been an in increase in various pipelines to analyze, classify and prioritize crisis-related data on various social media platforms. These pipelines utilize various data augmentation methods to counter imbalanced crisis data, sophisticated and off-the-shelf models for training. However, there is a lack of comprehensive study which compares these methods for the various sections of a pipeline. In this study, we split a general crisis-related pipeline into 3 major sections, namely, data augmentation, model selection, and training methodology. We compare various methods for each of these sections and then present a comprehensive evaluation of which section to prioritize based on the results from various pipelines. We compare our results against two separate tasks, information classification and priority scoring for crisis-related tweets. Our results suggest that data augmentation, in general,improves the performance. However, sophisticated, state-of-the-art language models like DeBERTa only show performance gain in information classification tasks, and models like RoBERTa tend to show a consistent performance increase over our presented baseline consisting of BERT. We also show that, though training two separate task-specific BERT models does show better performance than one BERT model with multi-task learning methodology over an imbalanced dataset, multi-task learning does improve performance for more sophisticated model like DeBERTa with a much more balanced dataset after augmentation.