SEIS: A spatiotemporal-aware event investigation framework for social airborne sensing in disaster recovery applications

Abstract

Social airborne sensing (SAS) is taking shape as a new integrated sensing paradigm that melds the human wisdom derived from social data platforms (e.g., Twitter, Facebook) with the empirical sensing capabilities of unmanned aerial vehicles (UAVs) for providing multifaceted information acquisition and situation awareness services in disaster recovery applications. A crucial task in the aftermath of a disaster is to determine the veracity of the reported events alongside assessing their underlying urgency that can possibly facilitate appropriate parties in their disaster mitigation and recovery efforts. For example, identifying a falsely reported event early on thats claims fatalities could help divert alleviation efforts to genuinely critical events. However, existing SAS schemes are limited to deducing only the veracity of the reports on social data platforms and are unable to infer the underlying urgency of the events. In this paper, we explore the opportunity to develop a spatiotemporal-aware event investigation framework for SAS that can jointly determine the veracity of reported events as well as infer their underlying urgency and deadlines. However, constructing such an integrated system introduces a few new technical challenges. The first challenge is handling the predominant data sparsity in the incoming social signals. The second challenge is optimizing the UAV deployment and event veracity estimation processes by scrutinizing the highly dynamic and latent correlations among event characteristics. The third challenge is carefully extracting and analyzing latent semantic features embedded in the social media data to infer the event urgency. To address the above challenges, we introduce the Spatiotemporal-aware Event Investigation for SAS (SEIS) framework that harnesses techniques from natural language processing (NLP), deep learning, and spatial–temporal correlation modeling for deducing the veracity, urgency and deadlines of the underlying events. Experiments through a real-world disaster recovery dataset demonstrate that SEIS achieves better event veracity estimation, event urgency inference, and deadline hit rate compared to state-of-the-art baselines.