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

By Muhammad Imran, Prasenjit Mitra, Carlos Castillo. International Conference on Language Resources and Evaluation, 2016

357 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Communication

View Paper: https://www.semanticscholar.org/paper/88e365645f3583b8efdcb80c838217841fa793c7

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Citations in the corpus

CrisisMMD: Multimodal Twitter Datasets from Natural Disasters (2018, background): “Recently published dataset by (Imran, Mitra, and Castillo 2016) consists of tweets from 19 different crisis events that took place between 2013 to 2015.”
RealTime QA: What's the Answer Right Now? (2022, background): “For example, a QA system with timely updates can improve emergency management of natural disasters (Imran et al., 2013, 2015, 2016; Nguyen et al., 2016) or pandemics (e.g., COVID-19; Wang et al., 2020; Lee et al., 2020; Möller et al., 2020; Alzubi et al., 2021).”
Dialect Diversity in Text Summarization on Twitter (2020, background): “Crises NLP dataset: This dataset contains crisis-related tweets from 19 diﬀerent crisis (eg, earthquakes, ﬂoods, etc.) around the world that took place 2013 to 2015 [31].”