False information has a significant negative influence on individuals as well
as on the whole society. Especially in the current COVID-19 era, we witness an
unprecedented growth of medical misinformation. To help tackle this problem
with machine learning approaches, we are publishing a feature-rich dataset of
approx. 317k medical news articles/blogs and 3.5k fact-checked claims. It also
contains 573 manually and more than 51k automatically labelled mappings between
claims and articles. Mappings consist of claim presence, i.e., whether a claim
is contained in a given article, and article stance towards the claim. We
provide several baselines for these two tasks and evaluate them on the manually
labelled part of the dataset. The dataset enables a number of additional tasks
related to medical misinformation, such as misinformation characterisation
studies or studies of misinformation diffusion between sources.

该研究发表了一篇医疗虚假信息数据集的机器学习处理方法，其中包含了 317k 篇医疗文章和 3.5k 个经过事实核查的声明，并且提供了 573 个人工标注和 51k 个自动标注的声明与文章之间的映射，这些映射包括声明是否出现在给定文章中以及文章对声明的态度。该数据集可以用于医疗虚假信息的特征研究和来自不同来源的虚假信息传播的研究。

Monant 医疗错误信息数据集：将文章映射到事实核查的声明

Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims

In 2019, outbreaks of vaccine-preventable diseases reached the highest number
in the US since 1992. Medical misinformation, such as antivaccine content
propagating through social media, is associated with increases in vaccine delay
and refusal. Our overall goal is to develop an automatic detector for
antivaccine messages to counteract the negative impact that antivaccine
messages have on the public health. Very few extant detection systems have
considered multimodality of social media posts (images, texts, and hashtags),
and instead focus on textual components, despite the rapid growth of
photo-sharing applications (e.g., Instagram). As a result, existing systems are
not sufficient for detecting antivaccine messages with heavy visual components
(e.g., images) posted on these newer platforms. To solve this problem, we
propose a deep learning network that leverages both visual and textual
information. A new semantic- and task-level attention mechanism was created to
help our model to focus on the essential contents of a post that signal
antivaccine messages. The proposed model, which consists of three branches, can
generate comprehensive fused features for predictions. Moreover, an ensemble
method is proposed to further improve the final prediction accuracy. To
evaluate the proposed model's performance, a real-world social media dataset
that consists of more than 30,000 samples was collected from Instagram between
January 2016 and October 2019. Our 30 experiment results demonstrate that the
final network achieves above 97% testing accuracy and outperforms other
relevant models, demonstrating that it can detect a large amount of antivaccine
messages posted daily. The implementation code is available at
this https URL

本研究旨在发展一种深度学习网络，利用视觉和文本信息以检测社交媒体中具有反疫苗信息的帖子，通过新的语义和任务层注意机制和三个分支的模型，本研究在 Instagram 实时社交媒体数据集上的实验表明，模型可以实现 97% 以上的测试准确性。