Extracting Large Scale Spatio-Temporal Descriptions from Social Media

TL;DR

This paper explores augmenting large-scale event tracking by integrating social media data with sensor information to improve real-time emergency response and understanding of natural disasters.

Contribution

It introduces a framework for combining social media with sensor data for enhanced spatio-temporal event descriptions, addressing challenges like noise, multilingualism, and geolocation.

Findings

Social media data can complement sensor information for better event detection.

Case studies demonstrate improved situational awareness during natural disasters.

Preliminary methods for event sensing and data integration are presented.

Abstract

The ability to track large-scale events as they happen is essential for understanding them and coordinating reactions in an appropriate and timely manner. This is true, for example, in emergency management and decision-making support, where the constraints on both quality and latency of the extracted information can be stringent. In some contexts, real-time and large-scale sensor data and forecasts may be available. We are exploring the hypothesis that this kind of data can be augmented with the ingestion of semi-structured data sources, like social media. Social media can diffuse valuable knowledge, such as direct witness or expert opinions, while their noisy nature makes them not trivial to manage. This knowledge can be used to complement and confirm other spatio-temporal descriptions of events, highlighting previously unseen or undervalued aspects. The critical aspects of this investigation, such as event sensing, multilingualism, selection of visual evidence, and geolocation, are currently being studied as a foundation for a unified spatio-temporal representation of multi-modal descriptions. The paper presents, together with an introduction on the topics, the work done so far on this line of research, also presenting case studies relevant to the posed challenges, focusing on emergencies caused by natural disasters.