Social-EOC: Serviceability Model to Rank Social Media Requests for Emergency Operation Centers

Abstract

The public expects a prompt response from emergency services to address requests for help posted on social media. However, the information overload of social media experienced by these organizations, coupled with their limited human resources, challenges them to timely identify and prioritize critical requests. This is particularly acute in crisis situations where any delay may have a severe impact on the effectiveness of the response. While social media has been extensively studied during crises, there is limited work on formally characterizing serviceable help requests and automatically prioritizing them for a timely response. In this paper, we present a formal model of serviceability called Social-EOC (Social Emergency Operations Center), which describes the elements of a serviceable message posted in social media that can be expressed as a request. We also describe a system for the discovery and ranking of highly serviceable requests, based on the proposed serviceability model. We validate the model for emergency services, by performing an evaluation based on real-world data from six crises, with ground truth provided by emergency management practitioners. Our experiments demonstrate that features based on the serviceability model improve the performance of discovering and ranking (nDCG up to 25%) service requests over different baselines. In the light of these experiments, the application of the serviceability model could reduce the cognitive load on emergency operation center personnel, in filtering and ranking public requests at scale.

Ranking of Social Media Alerts with Workload Bounds in Emergency Operation Centers

“…We consider a general class of emergency service requests as relevant messages for alerts that include actions, such as a request for resources (e.g., emergency medical assistance for an injured person) as well as information (e.g., a request for a phone number for information on missing people) [27], [7]. We have considered serviceability of messages as the relevance criterion [7]. The key characteristic of serviceability of a request message for an alert is that it requests a resource that can be provided, or asks a question that can be answered by the service personnel.…”

“…For features, we first used Bag-of-Words features that achieved accuracy of only 65%. Therefore, we resolved to an improved approach for the relevancy classification with better accuracy from our prior work [7] that used additional features of informative details, such as time, place, or context in the message content. Using the relevancy classification and ranking prediction for messages, we compute the ranking metrics for a given set of messages x ij in a period t ij for different types of ranking from top-1 to top-k alerts.…”

“…We borrowed the datasets of 6 crisis events (c.f. table III) from our prior work [7]. We processed them using the approach described in section III, within the periodic and realtime schemes.…”

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

“…To the best of our knowledge, this is the first comprehensive study to formally define and extensively analyze the application of a generic serviceability model (Social-EOC) for social media requests, in order to identify, prioritize, and group actionable requests to respond for emergency services. This paper builds upon our previous work (Purohit et al 2018), where we proposed the Social-EOC model and presented experiments to rank social media requests using a Learning-to-Rank framework.…”

Relevancy Classification of Multimodal Social Media Streams for Emergency Services

“…Hence, exploiting features from all the modalities in the data can be valuable and necessary to efficiently understand the actual meaning of the message. Content of such posts vary in potential value for operational response, ranging from actionable and serviceable requests [8] to potential rumors [9] to damage reports [10]. Thus, quickly filtering and prioritizing such multimodal messages with relevant information have become a critical need for response agencies [5].…”