Processing Social Media Messages in Mass Emergency: A Survey

TL;DR

This survey reviews computational methods for processing social media messages during emergencies, highlighting challenges like information overload and credibility filtering, and discussing solutions for event detection and summarization to aid emergency response.

Contribution

It provides a comprehensive overview of current techniques tailored for emergency scenarios, emphasizing the unique challenges and solutions in processing social media data during crises.

Findings

Various filtering and classification methods improve information relevance.

Event detection techniques enable timely situational awareness.

Summarization approaches help distill critical information for responders.

Abstract

Social media platforms provide active communication channels during mass convergence and emergency events such as disasters caused by natural hazards. As a result, first responders, decision makers, and the public can use this information to gain insight into the situation as it unfolds. In particular, many social media messages communicated during emergencies convey timely, actionable information. Processing social media messages to obtain such information, however, involves solving multiple challenges including: handling information overload, filtering credible information, and prioritizing different classes of messages. These challenges can be mapped to classical information processing operations such as filtering, classifying, ranking, aggregating, extracting, and summarizing. We survey the state of the art regarding computational methods to process social media messages, focusing on their application in emergency response scenarios. We examine the particularities of this setting, and then methodically examine a series of key sub-problems ranging from the detection of events to the creation of actionable and useful summaries.