A text mining analysis of human flourishing on Twitter

Abstract

The power of social media in spreading the idea of wellbeing has already been addressed by several psychologists and scholars through the analysis of the vocabulary; however, the use of the human flourishing (HF) concept in such platforms has not yet been analyzed. This study addresses such a topic by analyzing more than 600 thousand Twitter messages posted by a community of users who associate themselves with HF and comparing them to more than 400 thousand messages in other Twitter lists. The study aims to identify the HF users’ interests, the richness in their vocabulary, the feelings and emotions that they share, and the grammar used in their constructions. Such an analysis was conducted through text mining computational methods, including sentiment analysis, natural language processing (NLP), and topic modeling. The results revealed that although HF users employ average vocabulary diversity, they share more positive emotions, and a greater variety of emojis. They also tended to discuss different topics, from more spiritual and health-related subjects to more practical matters related to work and success. Finally, they generally wrote from an empathetic state of mind, caring about people’s day-to-day feelings and about the world.