VAST consists of a large range of topics covering broad themes, such as politics (e.g., ‘a Palestinian state’), education (e.g., ‘charter schools’), and public health (e.g., ‘childhood vaccination’). In addition, the data includes a wide range of similar expressions (e.g., ‘guns on campus’ versus ‘firearms on campus’). This variation captures how humans might realistically describe the same topic and contrasts with the lack of variation in existing datasets.
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MMVax-Stance includes 113 Vaccine Hesitancy Framings found on Twitter about the COVID-19 vaccines. Language experts annotated multimodal image-text tweets as Relevant or Not Relevant, and then further annotated Relevant tweets with Stance towards each framing.
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