Processing Social Media Messages in Mass Emergency: A Survey

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.

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Cited by in corpus (11)

• A Survey of Location Prediction on Twitter
• Geo-knowledge-guided GPT models improve the extraction of location descriptions from disaster-related social media messages
• Mining Social Media to Inform Peatland Fire and Haze Disaster Management
• Keyphrase Extraction from Disaster-related Tweets
• RweetMiner: Automatic identification and categorization of help requests on twitter during disasters
• Identifying emergency stages in Facebook posts of police departments with convolutional and recurrent neural networks and support vector machines
• How to Save Lives with Microblogs? Lessons From the Usage of Weibo for Requests for Medical Assistance During COVID-19
• CrisisViT: A Robust Vision Transformer for Crisis Image Classification
• A Twitter narrative of the COVID-19 pandemic in Australia
• EnDSUM: Entropy and Diversity based Disaster Tweet Summarization
• A system for the 2019 Sentiment, Emotion and Cognitive State Task of DARPAs LORELEI project