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Ranking of social media alerts with workload bounds in emergency operation centers

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dc.contributor.author Purohit, Hemant
dc.contributor.author Castillo, Carlos
dc.contributor.author Imran, Muhammad
dc.contributor.author Pandey, Rahul
dc.date.accessioned 2019-03-20T17:37:22Z
dc.date.available 2019-03-20T17:37:22Z
dc.date.issued 2018
dc.identifier.citation Purohit H, Castillo C, Imran M, Pandey R. Ranking of social media alerts with workload bounds in emergency operation centers. In: Proceedings 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI). 2018 Dec 3–6; Santiago, Chile. Los Alamitos, CA: IEEE; 2018. p. 206-13. DOI: 10.1109/WI.2018.00-88
dc.identifier.isbn 978-1-5386-7326-3
dc.identifier.uri http://hdl.handle.net/10230/36873
dc.description Comunicació presentada a IEEE/WIC/ACM International Conference on Web Intelligence (WI), celebrada del 3 al 6 de desembre de 2018 a Santiago, Xile.
dc.description.abstract Extensive research on social media usage during emergencies has shown its value to provide life-saving information, if a mechanism is in place to filter and prioritize messages. Existing ranking systems can provide a baseline for selecting which updates or alerts to push to emergency responders. However, prior research has not investigated in depth how many and how often should these updates be generated, considering a given bound on the workload for a user due to the limited budget of attention in this stressful work environment. This paper presents a novel problem and a model to quantify the relationship between the performance metrics of ranking systems (e.g., recall, NDCG) and the bounds on the user workload. We then synthesize an alert-based ranking system that enforces these bounds to avoid overwhelming end-users. We propose a Pareto optimal algorithm for ranking selection that adaptively determines the preference of top-k ranking and user workload over time. We demonstrate the applicability of this approach for Emergency Operation Centers (EOCs) by performing an evaluation based on real world data from six crisis events. We analyze the trade-off between recall and workload recommendation across periodic and realtime settings. Our experiments demonstrate that the proposed ranking selection approach can improve the efficiency of monitoring social media requests while optimizing the need for user attention.
dc.description.sponsorship Purohit thanks US National Science Foundation grants IIS-1657379 & IIS-1815459 and Castillo thanks La Caixa project LCF/PR/PR16/11110009 for partial support.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof Proceedings 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI). 2018 Dec 3–6; Santiago, Chile. Los Alamitos, CA: IEEE; 2018. p. 206-13.
dc.rights © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The final published article can be found at https://ieeexplore.ieee.org/document/8609595
dc.title Ranking of social media alerts with workload bounds in emergency operation centers
dc.type info:eu-repo/semantics/conferenceObject
dc.identifier.doi http://dx.doi.org/10.1109/WI.2018.00-88
dc.subject.keyword Human-centered computing
dc.subject.keyword Information overload
dc.subject.keyword Attention budget
dc.subject.keyword Disaster management
dc.subject.keyword Pareto optimality
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/acceptedVersion

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