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Social media is often utilized as a lifeline for communication during naturaldisasters. Traditionally, natural disaster tweets are filtered from the Twitterstream using the name of the natural disaster and the filtered tweets are sentfor human annotation. The process of human annotation to create labeled setsfor machine learning models is laborious, time consuming, at times inaccurate,and more importantly not scalable in terms of size and real-time use. In thiswork, we curate a silver standard dataset using weak supervision. In order tovalidate its utility, we train machine learning models on the weakly superviseddata to identify three different types of natural disasters i.e earthquakes,hurricanes and floods. Our results demonstrate that models trained on thesilver standard dataset achieved performance greater than 90% when classifyinga manually curated, gold-standard dataset. To enable reproducible research andadditional downstream utility, we release the silver standard dataset for thescientific community.

TweetDIS: A Large Twitter Dataset for Natural Disasters Built using Weak Supervision