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 \textit{ 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
Time-critical analysis of social media streams is important for URLanizations for planing rapid response during disasters. The crisis informatics research community has developed several techniques and systems for processing and classifying big crisis-related data posted on social media. However, due to the dispersed nature of the datasets used in the literature (e.g., for training models), it is not possible to compare the results and measure the progress made towards building better models for crisis informatics tasks . In this work, we attempt to bridge this gap by combining various existing crisis-related datasets. We consolidate eight human-annotated datasets and provide 166.1k and 141.5k tweets for informativeness and\textit{humanitarian classification tasks, respectively. We believe that the consolidated dataset will help train more sophisticated models. Moreover , we provide benchmarks for both binary and multiclass classification tasks using several deep learning architecrures including, CNN, fastText, and transformers . We make the dataset and scripts available at: URL
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to plan
for planing
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crisis informatics
crisis informatics
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to process and classify big crisis related
for processing and classifying big crisis-related
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,
(e.g., for training models),
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building
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tasks
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standardizing
combining
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labels of eight annotated data sources
eight human-annotated datasets
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informativeness and humanitarian
informativeness
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and
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humanitarian
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The consolidation results in a larger dataset that affords the ability to
We believe that the consolidated dataset will help
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To that end
Moreover
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baseline results using CNN and BERT models
benchmarks for both binary and multiclass classification tasks using several deep learning architecrures including, CNN, fastText, and transformers
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available at
and scripts available at: