Steve Peterson, Keri Stephens, Hemant Purohit, & Amanda Hughes. (2019). When Official Systems Overload: A Framework for Finding Social Media Calls for Help during Evacuations. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: During large-scale disasters it is not uncommon for Public Safety Answering Points (e.g., 9-1-1) to encounter
service disruptions or become overloaded due to call volume. As observed in the two past United States hurricane
seasons, citizens are increasingly turning to social media whether as a consequence of their inability to reach
9-1-1, or as a preferential means of communications. Relying on past research that has examined social media
use in disasters, combined with the practical knowledge of the first-hand disaster response experiences, this paper
develops a knowledge-driven framework containing parameters useful in identifying patterns of shared
information on social media when citizens need help. This effort explores the feasibility of determining
differences, similarities, common themes, and time-specific discoveries of social media calls for help associated
with hurricane evacuations. At a future date, validation of this framework will be demonstrated using datasets
from multiple disasters. The results will lead to recommendations on how the framework can be modified to make
it applicable as a generic disaster-type characterization tool.
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Fedor Vitiugin, & Carlos Castillo. (2019). Comparison of Social Media in English and Russian During Emergencies and Mass Convergence Events. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: Twitter is used for spreading information during crisis events. In this paper, we first retrieve event-related information
posted in English and Russian during six disasters and sports events that received wide media coverage in both
languages, using an adaptive information filtering method for automating the collection of about 100 000 messages.
We then compare the contents of these messages in terms of 17 informational and linguistic features using a
difference in differences approach. Our results suggest that posts in each language are focused on different types
of information. For instance, almost 50% of the popular people mentioned in these messages appear exclusively
in either the English messages or the Russian messages, but not both. Our results also suggest differences in the
adoption of platform mechanics during crises between Russian-speaking and English-speaking users. This has
important implications for data collection during crises, which is almost always focused on a single language.
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Asmelash Teka Hadgu, Sallam Abualhaija, & Claudia Niederée. (2019). Real-time Adaptive Crawler for Tracking Unfolding Events on Twitter. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: When a major event such as a crisis situation occurs, people post messages on social media sites such as Twitter, in
order to exchange information or to share emotions. These posts can provide useful information to raise situation
awareness and support decision making, e.g., by aid organizations. In this paper, we propose a novel method for
social media crawling, which exploits a Bayesian inference framework to keep track of keyword changes over time
and uses a counter-stream to gauge the inclusion of noise and irrelevant information. In addition, we present a
framework to evaluate real-time adaptive social search algorithms in a reproducible manner, which relies on a
semi-automated approach for ground-truth construction. We show that our method outperforms previous methods
for very large scale events.
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Jess Kropczynski, Rob Grace, Shane Halse, Doina Caragea, Cornelia Caragea, & Andrea Tapia. (2019). Refining a Coding Scheme to Identify Actionable Information on Social Media. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: This paper describes the use of a previously established qualitative coding scheme developed through a design workshop with public safety professionals, and applied the schema to social media data collecting during crises. The intention of applying this scheme to existing crisis datasets was to acquire training data for machine learning. Applying the coding scheme to social media data revealed that additional subcategories of the coding scheme are necessary to satisfy information requirements necessary to dispatch first responders to an incident. The coding scheme was refined and adapted into a set of instructions for qualitative coders on Amazon Mechanical Turk. The contribution of this work is a coding scheme that is more directly related to the information needs of public safety professionals. Implications of early results using the refined coding scheme are discussed in terms of proposed automated methods to identify actionable information for dispatch of first responders during emergency incidents.
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Gabriela C Barrera, & Maria C Yang. (2019). Evaluation of Digital Volunteers using a Design Approach: Motivations and Contributions in Disaster Response. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: With the growth of social media and crowdsourcing in disaster response, further research is needed on the motivations
and contributions of digital volunteers. This study applies a user-centered design approach to understanding how we
might make better tools to support digital volunteers. This user-centered design approach involves stated preference
elicitation methods through an online survey to understand what digital volunteers want in such tools. Through
choice-based conjoint analysis, we contribute to mixed-methods research to gain additional insight into motivations
and user preferences for a set of design features that might be incorporated into an online tool specifically for digital
volunteers. Initial results show preferences for measures of success that were not monetary, which aligned with
directly stated motivations for volunteering. Our findings corroborate with previous research in that feedback to
volunteers is very important, as well as being able to measure the impact of their work.
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