# Processing Social Media Messages in Mass Emergency: A Survey

arXiv: https://arxiv.org/abs/1407.7071 · HTML: https://mlsanity.com/paper/1407.7071/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2014.

## Built on by (most cited first, 30 of 51)

- [RealTime QA: What's the Answer Right Now?](https://mlsanity.com/paper/2207.13332/) (2022)
- [HumAID: Human-Annotated Disaster Incidents Data from Twitter with Deep Learning Benchmarks](https://mlsanity.com/paper/2104.03090/) (2021)
- [Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidents](https://mlsanity.com/paper/2201.04236/) (2022)
- [MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification](https://mlsanity.com/paper/2108.12828/) (2021)
- [Harnessing Large Language Models for Disaster Management: A Survey](https://mlsanity.com/paper/2501.06932/) (2025)
- [A social context-aware graph-based multimodal attentive learning framework for disaster content classification during emergencies: a benchmark dataset and method](https://mlsanity.com/paper/2410.08814/) (2024)
- [DeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet Classification via Memory Bank](https://mlsanity.com/paper/2310.14577/) (2023)
- [CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts](https://mlsanity.com/paper/2309.05494/) (2023)
- [Evaluating Robustness of LLMs on Crisis-Related Microblogs across Events, Information Types, and Linguistic Features](https://mlsanity.com/paper/2412.10413/) (2024)
- [Socially Enhanced Situation Awareness from Microblogs using Artificial Intelligence: A Survey](https://mlsanity.com/paper/2209.07272/) (2022)
- [Geo-Information Harvesting from Social Media Data](https://mlsanity.com/paper/2211.00543/) (2022)
- [RweetMiner: Automatic identification and categorization of help requests on twitter during disasters](https://mlsanity.com/paper/2303.02399/) (2023)
- [Leave no Place Behind: Improved Geolocation in Humanitarian Documents](https://mlsanity.com/paper/2309.02914/) (2023)
- [CrisisViT: A Robust Vision Transformer for Crisis Image Classification](https://mlsanity.com/paper/2401.02838/) (2024)
- [Robust Training of Social Media Image Classification Models for Rapid Disaster Response](https://mlsanity.com/paper/2104.04184/) (2021)
- [CReMa: Crisis Response through Computational Identification and Matching of Cross-Lingual Requests and Offers Shared on Social Media](https://mlsanity.com/paper/2405.11897/) (2024)
- [Multi-View Active Learning for Short Text Classification in User-Generated Data](https://mlsanity.com/paper/2112.02611/) (2021)
- [Integrating Human-in-the-loop into Swarm Learning for Decentralized Fake News Detection](https://mlsanity.com/paper/2201.02048/) (2022)
- [Cross-Lingual Query-Based Summarization of Crisis-Related Social Media: An Abstractive Approach Using Transformers](https://mlsanity.com/paper/2204.10230/) (2022)
- [Monitoring Critical Infrastructure Facilities During Disasters Using Large Language Models](https://mlsanity.com/paper/2404.14432/) (2024)
- [DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster Management](https://mlsanity.com/paper/2505.15856/) (2025)
- [Multi-task transfer learning for finding actionable information from crisis-related messages on social media](https://mlsanity.com/paper/2102.13395/) (2021)
- [Developing Annotated Resources for Internal Displacement Monitoring](https://mlsanity.com/paper/2104.05459/) (2021)
- [Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts](https://mlsanity.com/paper/2403.16614/) (2024)
- [DMRetriever: A Family of Models for Improved Text Retrieval in Disaster Management](https://mlsanity.com/paper/2510.15087/) (2025)
- [DisastQA: A Comprehensive Benchmark for Evaluating Question Answering in Disaster Management](https://mlsanity.com/paper/2601.03670/) (2026)
- [Is MT Ready for the Next Crisis or Pandemic?](https://mlsanity.com/paper/2601.10082/) (2026)
- [Identification of Fine-Grained Location Mentions in Crisis Tweets](https://mlsanity.com/paper/2111.06334/) (2021)
- [CrisisMatch: Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification](https://mlsanity.com/paper/2310.14627/) (2023)
- [Flood Event Extraction from News Media to Support Satellite-Based Flood Insurance](https://mlsanity.com/paper/2312.14943/) (2023)

## Abstract (arXiv)

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.