Ranking and grouping social media requests for emergency services using serviceability model
Bibliographic Data
| ID | 4682648 |
|---|---|
| Authors | Hemant Purohit (0000-0002-4573-8450, George Mason University, corresponding author), Carlos Castillo (0000-0001-5848-0332, Universitat Pompeu Fabra), Rahul Pandey (0000-0001-7766-3572, George Mason University) |
| Year | 2020 |
| Volume | 10 |
| Issue | 1 |
| Publication date | 2020-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Social Network Analysis and Mining (JOURNAL) |
| Journal identifiers | ISSN: 1869-5450 • E-ISSN: 1869-5469 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s13278-020-0633-3 |
| OpenAlex | W3013568625 |
| Language | EN |
| Citations received | 8 |
| References cited | 35 |
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 first present a formal model of serviceability calledSocial-EOC, which describes the elements of aserviceablemessage 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 (nDCGgain up to 25%) service requests over different baselines. We also empirically validate the existence of redundancy and semantic coherence among the serviceable requests using our semantic grouping approach, which shows the significance 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 filtering, ranking, and organizing public requests on social media at scale
Information retrieval · Learning to rank · Serviceability (structure) · Social connectedness · Social media · World Wide Web · Computer Science · Engineering · Public Relations and Crisis Communication · Sentiment Analysis and Opinion Mining · Topic Modeling
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| Unique citing works | 8 |
|---|---|
| Citations per year | 1.33 |
| Citation span | 2020 - 2024 (5) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 7 |