The widespread use of social media allows unprecedented ways to monitor opinions and stances regarding critical public health issues globally. Advanced Natural Language processing algorithms are being used routinely to extract information and classify vaccination hesitancy or stance. However, communication on online social networks such as Twitter (now X) is carried by short messages, the meaning of which can be difficult to understand in the absence of context. Therefore, in this study we propose the use of complex-network features extracted from the social network to integrate and enhance text-based Deep Learning models. Leveraging a dataset of about 20 million Italian language posts (of which about 7000 were manually annotated), we showed how the integration of text and network features improves vaccine stance classification, especially for the most polarized classes. Additionally, network features overperformed text features in a dataset collected a year after model training, possibly indicating how the social network changes more slowly than the trending words or topics.
Francesco et al. (Mon,) studied this question.