Social media has become a crucial platform for communication and information sharing, but it also facilitates the spread of misinformation, particularly during crises such as the COVID-19 pandemic undermining public’s trust in science. Especially, battling misinformation on scientific issues is important to bring closer public to science. The present work is a part of the EU funded project VERITY that aims to address this challenge by using a multidisciplinary approach resulting in in development of strategies to enhance public’s trust in science. Among other methodologies, VERITY investigates social media to identify factors that influence trust in science, and this article provides the results of a study on a specific dataset related to COVID-19 vaccination through various methods such as social network analysis, quantitative and qualitative analysis, analysis of the content and the reactions of the messages and the application of deep learning models in order to reveal people’s behaviour and the factors that influence it.

The widespread use of social networks has brought to the fore a very important issue, the reliability of the information circulating within them. This paper presents the developed technologies referring to the visualization of data coming from social media involving the parameter of trustworthiness, and demonstrates the solution of H2020 EUNOMIA project. In particular, EUNOMIA's Digital Observatory is comprised of two tools drawing data from a REST API structure that returns the data in JSON format. A tool provides visualizations about the post's sentiment accompanied by their votes of trustworthiness and a second tool that depicts the most shared posts illustrating their trustworthiness according to their votes in an interactive way that permits user to define the level of trustworthiness.