Insta-VAX: A Multimodal Benchmark for Anti-Vaccine and Misinformation Posts Detection on Social Media

• URL: http://arxiv.org/abs/2112.08470v1
• Date: Wed, 15 Dec 2021 20:34:57 GMT
• Title: Insta-VAX: A Multimodal Benchmark for Anti-Vaccine and Misinformation Posts Detection on Social Media
• Authors: Mingyang Zhou, Mahasweta Chakraborti, Sijia Qian, Zhou Yu, Jingwen Zhang
• Abstract summary: Anti-vaccine posts on social media have been shown to create confusion and reduce the publics confidence in vaccines. Insta-VAX is a new multi-modal dataset consisting of a sample of 64,957 Instagram posts related to human vaccines.
• Score: 32.252687203366605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sharing of anti-vaccine posts on social media, including misinformation posts, has been shown to create confusion and reduce the publics confidence in vaccines, leading to vaccine hesitancy and resistance. Recent years have witnessed the fast rise of such anti-vaccine posts in a variety of linguistic and visual forms in online networks, posing a great challenge for effective content moderation and tracking. Extending previous work on leveraging textual information to understand vaccine information, this paper presents Insta-VAX, a new multi-modal dataset consisting of a sample of 64,957 Instagram posts related to human vaccines. We applied a crowdsourced annotation procedure verified by two trained expert judges to this dataset. We then bench-marked several state-of-the-art NLP and computer vision classifiers to detect whether the posts show anti-vaccine attitude and whether they contain misinformation. Extensive experiments and analyses demonstrate the multimodal models can classify the posts more accurately than the uni-modal models, but still need improvement especially on visual context understanding and external knowledge cooperation. The dataset and classifiers contribute to monitoring and tracking of vaccine discussions for social scientific and public health efforts in combating the problem of vaccine misinformation.

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