# Twitter as a Lifeline: Human-annotated Twitter Corpora for NLP of Crisis-related Messages

arXiv: https://arxiv.org/abs/1605.05894 · HTML: https://mlsanity.com/paper/1605.05894/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2016.

## Built on by (most cited first, 30 of 44)

- [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)
- [The Surprising Performance of Simple Baselines for Misinformation Detection](https://mlsanity.com/paper/2104.06952/) (2021)
- [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)
- [Investigating label suggestions for opinion mining in German Covid-19 social media](https://mlsanity.com/paper/2105.12980/) (2021)
- [HumSet: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crisis Response](https://mlsanity.com/paper/2210.04573/) (2022)
- [CrisisLTLSum: A Benchmark for Local Crisis Event Timeline Extraction and Summarization](https://mlsanity.com/paper/2210.14190/) (2022)
- [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)
- [Geo-Information Harvesting from Social Media Data](https://mlsanity.com/paper/2211.00543/) (2022)
- [Collect, Measure, Repeat: Reliability Factors for Responsible AI Data Collection](https://mlsanity.com/paper/2308.12885/) (2023)
- [Cross-Lingual and Cross-Domain Crisis Classification for Low-Resource Scenarios](https://mlsanity.com/paper/2209.02139/) (2022)
- [PORTRAIT: a hybrid aPproach tO cReate extractive ground-TRuth summAry for dIsaster evenT](https://mlsanity.com/paper/2305.11536/) (2023)
- [IKDSumm: Incorporating Key-phrases into BERT for extractive Disaster Tweet Summarization](https://mlsanity.com/paper/2305.11592/) (2023)
- [Near-real-time Earthquake-induced Fatality Estimation using Crowdsourced Data and Large-Language Models](https://mlsanity.com/paper/2312.03755/) (2023)
- [A multi-modal approach towards mining social media data during natural disasters -- a case study of Hurricane Irma](https://mlsanity.com/paper/2101.00480/) (2021)
- [Developing Annotated Resources for Internal Displacement Monitoring](https://mlsanity.com/paper/2104.05459/) (2021)
- [TweetDIS: A Large Twitter Dataset for Natural Disasters Built using Weak Supervision](https://mlsanity.com/paper/2207.04947/) (2022)
- [Leveraging Domain Knowledge for Inclusive and Bias-aware Humanitarian Response Entry Classification](https://mlsanity.com/paper/2305.16756/) (2023)
- [Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts](https://mlsanity.com/paper/2403.16614/) (2024)
- [Zero-Shot Classification of Crisis Tweets Using Instruction-Finetuned Large Language Models](https://mlsanity.com/paper/2410.00182/) (2024)
- [Intelligent Agent for Hurricane Emergency Identification and Text Information Extraction from Streaming Social Media Big Data](https://mlsanity.com/paper/2106.07114/) (2021)
- [Identification of Fine-Grained Location Mentions in Crisis Tweets](https://mlsanity.com/paper/2111.06334/) (2021)
- [Flood Event Extraction from News Media to Support Satellite-Based Flood Insurance](https://mlsanity.com/paper/2312.14943/) (2023)
- [ATSumm: Auxiliary information enhanced approach for abstractive disaster Tweet Summarization with sparse training data](https://mlsanity.com/paper/2405.06541/) (2024)
- [Multilingual Topic Classification in X: Dataset and Analysis](https://mlsanity.com/paper/2410.03075/) (2024)
- [Extending a Parliamentary Corpus with MPs' Tweets: Automatic Annotation and Evaluation Using MultiParTweet](https://mlsanity.com/paper/2512.11567/) (2025)
- [Active Hybrid Classification](https://mlsanity.com/paper/2101.08854/) (2021)
- [Bridging the gap between supervised classification and unsupervised topic modelling for social-media assisted crisis management](https://mlsanity.com/paper/2103.11835/) (2021)

## Abstract (arXiv)

Microblogging platforms such as Twitter provide active communication channels during mass convergence and emergency events such as earthquakes, typhoons. During the sudden onset of a crisis situation, affected people post useful information on Twitter that can be used for situational awareness and other humanitarian disaster response efforts, if processed timely and effectively. Processing social media information pose multiple challenges such as parsing noisy, brief and informal messages, learning information categories from the incoming stream of messages and classifying them into different classes among others. One of the basic necessities of many of these tasks is the availability of data, in particular human-annotated data. In this paper, we present human-annotated Twitter corpora collected during 19 different crises that took place between 2013 and 2015. To demonstrate the utility of the annotations, we train machine learning classifiers. Moreover, we publish first largest word2vec word embeddings trained on 52 million crisis-related tweets. To deal with tweets language issues, we present human-annotated normalized lexical resources for different lexical variations.