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Ranking and grouping social media requests for emergency services using serviceability model

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dc.contributor.author Purohit, Hemant
dc.contributor.author Castillo, Carlos
dc.contributor.author Pandey, Rahul
dc.date.accessioned 2020-07-30T07:56:46Z
dc.date.available 2020-07-30T07:56:46Z
dc.date.issued 2020
dc.identifier.citation Purohit H, Castillo C, Pandey R. Ranking and grouping social media requests for emergency services using serviceability model. Soc Netw Anal Min. 2020;10:22. DOI: 10.1007/s13278-020-0633-3
dc.identifier.issn 1869-5450
dc.identifier.uri http://hdl.handle.net/10230/45222
dc.description.abstract Social media has become an alternative communication mechanism for the public to reach out to emergency services during time-sensitive events. However, the information overload of social media experienced by these services, coupled with their limited human resources, challenges them to timely identify, prioritize, and organize critical requests for help. In this paper, we frst present a formal model of serviceability called Social-EOC, which describes the elements of a serviceable message posted in social media expressing a request. Using the serviceability model, we then describe a system for the discovery and ranking of highly serviceable requests as well as for re-ranking requests by semantic grouping to reduce redundancy and facilitate the browsing of requests by responders. We validate the model for emergency services by experimenting with six crisis event datasets and ground truth provided by emergency professionals. Our experiments demonstrate that features based on both serviceability model and social connectedness improve the performance of discovering and ranking (nDCG gain up to 25%) service requests over diferent baselines. We also empirically validate the existence of redundancy and semantic coherence among the serviceable requests using our semantic grouping approach, which shows the signifcance and need for grouping similar requests to save the time of emergency services. Thus, an application of serviceability model could reduce cognitive load on emergency servicers in fltering, ranking, and organizing public requests on social media at scale.
dc.description.sponsorship We thank Yogen Chaudhari, Sharan Sai Banola, and Mohammad Rana for helping in data collection. Purohit thanks US National Science Foundation Grants IIS-1657379 and 1815459, and Castillo thanks La Caixa project LCF/PR/PR16/11110009 for partial support to this research.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Springer
dc.relation.ispartof Social Network Analysis and Mining. 2020;10:22
dc.rights © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title Ranking and grouping social media requests for emergency services using serviceability model
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1007/s13278-020-0633-3
dc.subject.keyword Information overload
dc.subject.keyword Serviceability
dc.subject.keyword Social media
dc.subject.keyword Emergency management
dc.subject.keyword Semantic grouping
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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