Social media platforms such as Twitter have a fundamental role in facilitating the spread and discussion of ideas online through the concept of retweeting and replying. However, these features also contribute to the spread of mis/disinformation during the vaccine rollout of the COVID-19 pandemic. Using COVID-19 vaccines as a case study, we analyse multiple social network representation derived from three message-based interactions on Twitter (quote retweets, mentions and replies) based upon a set of known anti-vax hashtags and keywords. Each network represents a certain hashtag or keyword which were labelled as "controversial" and "non-controversial" according to a small group of participants. For each network, we extract a combination of global and local network-based metrics which are used as feature vectors for binary classification. Our results suggest that it is possible to detect controversial from non-controversial terms with high accuracy using simple network-based metrics. Furthermore, these results demonstrate the potential of network representations as language-agnostic models for detecting mis/disinformation at scale, irrespective of content and across multiple social media platforms.

翻译：社交媒体平台（如Twitter）通过转发和回复的概念，在促进在线思想的传播与讨论中发挥着根本性作用。然而，这些功能也助长了COVID-19疫苗推广期间错误/虚假信息的传播。以COVID-19疫苗为案例研究，我们基于一组已知的反疫苗标签和关键词，分析了从Twitter上三种消息交互（引用转推、提及和回复）衍生出的多种社交网络表示。每个网络代表一个特定的标签或关键词，并根据少数参与者将其标记为"争议性"和"非争议性"。针对每个网络，我们提取全局和局部网络指标的组合作为二元分类的特征向量。研究结果表明，使用简单的网络指标能够以高准确率区分争议性与非争议性术语。此外，这些结果证明了网络表示作为语言无关模型在跨内容、跨多个社交媒体平台大规模检测错误/虚假信息方面的潜力。