Salemi, H., Senarath, Y., & Purohit, H. (2023). A Comparative Study of Pre-trained Language Models to Filter Informative Code-mixed Data on Social Media during Disasters. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 920–932). Omaha, USA: University of Nebraska at Omaha.
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.
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Grace, R., Montarnal, A., Petitdemange, E., Rutter, J., Rodriguez, G. R., & Potts, M. (2023). Collaborative Information Seeking during a 911 Call Surge: A Case Study. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 649–662). Omaha, USA: University of Nebraska at Omaha.
Abstract: This case study examines collaborative information seeking in a public-safety answering point during a 911 call surge that occurred when a man fired an assault rifle at police officers and evaded capture for nearly an hour in March 2020. Overwhelmed by questionable and imprecise reports from 911 callers, telecommunicators and on scene responders began working together to conduct broad and deep searches for the shooter. Whereas broad searches improved the scope of information gathering by identifying multiple, albeit questionable and imprecise, reports of the suspect’s location, deep searches improved the quality of information gathering by investigating 911 callers’ reports using drone, helicopter, and patrol units. These findings suggest requirements for collaborative information seeking in public-safety answering points, including capabilities to conduct broad and deep searches using next-generation 911 technologies, and command and control requirements for triaging these search tasks within inter-organizational emergency response systems.
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Wang, D., & Kogan, M. (2023). Resonance+: Augmenting Collective Attention to Find Information on Public Cognition and Perception of Risk. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 487–500). Omaha, USA: University of Nebraska at Omaha.
Abstract: Microblogging platforms have been increasingly used by the public and crisis managers in crisis. The increasing volume of data has made such platforms more difficult for officials to find on-the-ground information and understand the public’s perception of the evolving risks. The crisis informatics literature has proposed various technological solutions to find relevant information from social media. However, the cognitive processes of the affected population and their subsequent responses, such as perceptions, emotional and behavioral responses, are still under-examined at scale. Yet, such information is important for gauging public perception of risks, an important task for PIOs and emergency managers. In this work, we leverage the noise-cutting power of collective attention and take cues from the Protective Action Decision Model, to propose a method that estimates shifts in collective attention with a special focus on the cognitive processes of those affected and their subsequent responses.
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Nurollahian, S., Talegaonkar, I., Bell, A. Z., & Kogan, M. (2023). Factors Affecting Public’s Engagement with Tweets by Authoritative Sources During Crisis. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 459–477). Omaha, USA: University of Nebraska at Omaha.
Abstract: People increasingly use social media at the time of crisis, which produces a social media data deluge, where the public may find it difficult to locate trustworthy and credible information. Therefore, they often turn to authoritative sources: official individuals and organizations who are trusted to provide reliable information. It is then imperative that their credible messages reach and engage the widest possible audience, especially among those affected. In this study, we explore the role of metadata and linguistic factors in facilitating three types of engagement — retweets, replies, and favorites— with posts by authoritative sources. We find that many factors are similarly important across models (popularity, sociability, activity). However, some features are salient for only a specific type of engagement. We conclude by providing guidance to authoritative sources on how they may optimize specific types of engagement: retweets for information propagation, replies for in-depth sense-making, and favorites for cross-purpose visibility.
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Herrera, L. C., & Gjøsæter, T. (2023). Leveraging Crisis Informatics Experts: A co-creating approach for validation of social media research insights. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 439–448). Omaha, USA: University of Nebraska at Omaha.
Abstract: Validation of findings is a challenge in practice-based research. While analysis is being conducted and findings are being constructed out of data collected in a defined period, practitioners continue with their activities. This issue is exacerbated in the field of crisis management, where high volatility and personnel turnover make the capacity to attend research demands scarce. Therefore, conducting classic member validation is logistically challenging for the researcher. The need for rigor and validity calls for alternative mechanisms to fulfill requirements for academic research. This article presents an approach for validation of results of a qualitative study with public organizations that use social media as a source of information in the context of crisis management. The unavailability of original interview-objects to validate our findings resulted in an alternative validation method that leveraged experts in crisis informatics. By presenting our approach, we contribute to encouraging rigor in qualitative research while maintaining the relationship between practice and academia.
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