Using social media for sub-event detection during disasters

Loris Belcastro,Fabrizio Marozzo,Paolo Trunfio,Muhammad Imran,Themis Palpanas,Domenico Talia,Francesco Branda

doi:10.1186/s40537-021-00467-1

Loris Belcastro, Fabrizio Marozzo + Show 5 more

Open Access

https://doi.org/10.1186/s40537-021-00467-1

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Abstract

Social media platforms have become fundamental tools for sharing information during natural disasters or catastrophic events. This paper presents SEDOM-DD (Sub-Events Detection on sOcial Media During Disasters), a new method that analyzes user posts to discover sub-events that occurred after a disaster (e.g., collapsed buildings, broken gas pipes, floods). SEDOM-DD has been evaluated with datasets of different sizes that contain real posts from social media related to different natural disasters (e.g., earthquakes, floods and hurricanes). Starting from such data, we generated synthetic datasets with different features, such as different percentages of relevant posts and/or geotagged posts. Experiments performed on both real and synthetic datasets showed that SEDOM-DD is able to identify sub-events with high accuracy. For example, with a percentage of relevant posts of 80% and geotagged posts of 15%, our method detects the sub-events and their areas with an accuracy of 85%, revealing the high accuracy and effectiveness of the proposed approach.

Highlights

Social media platforms have become an important source of information that can be exploited to understand human dynamics and behaviors
Several experiments performed on both real and synthetic datasets showed that SEDOM-DD is able to identify sub-events with high accuracy both in detecting the area where they took place and in understanding the type of problem
The evaluation was carried out on synthetic datasets by using different configuration values for the parameters reported in Table 4, some of which were extracted from real Twitter data as described in [48]

Summary

Introduction

Social media platforms have become an important source of information that can be exploited to understand human dynamics and behaviors. Such datasets were generated starting from real social media posts published during or immediately after catastrophic events Some of these synthetic posts are marked with precise geographic coordinates, others are not geotagged but contain information that can be used to estimate their coordinates with a varying degree of precision, and the remaining ones generically refer to the main disaster but not to any sub-events. In order to obtain a real situation, not all generated posts are geotagged: only a small part of them include a geographic position or contain textual information that allows to estimate, with a certain precision, where the sub-event occurred. A label describing the occurred subevent is assigned to each cluster

Results

The earthquake tonight was truly severe

Conclusions