Time-critical analysis of social media streams is important for URLanizations to plan rapid response during disasters. The crisis informatics research community has developed several techniques and systems to process and classify big crisis data on social media. However, due to a variety of different datasets used in the literature, it is not possible to compare the results and to measure the progress made towards better models for crisis classification . In this work, we attempt to bridge this gap by providing a standard crisis-related dataset . We consolidate labels of 8 annotated data sources and provide 166.1k and 141.5k tweets for informativeness and humanitarian classification tasks  . The consolidation also result in larger dataset size which is helpful in training stronger models. We also provide baseline results using CNN and BERT models. We make the dataset available at URL
Time-critical analysis of social media streams is important for URLanizations to plan rapid response during disasters. The crisis informatics research community has developed several techniques and systems to process and classify big crisis related data posted on social media. However, due to the dispersed nature of the datasets used in the literature, it is not possible to compare the results and measure the progress made towards better models for crisis informatics . In this work, we attempt to bridge this gap by standardizing various existing crisis-related datasets . We consolidate labels of eight annotated data sources and provide 166.1k and 141.5k tweets for informativeness and humanitarian classification tasks , respectively . The consolidation results in a larger dataset that affords the ability to train more sophisticated models. To that end, we provide baseline results using CNN and BERT models. We make the dataset available at URL
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results in a larger dataset that affords the ability to train more sophisticated models. To that end, we