Exploring social trends in cohousing and coliving discussions on X(Twitter) using NLP and Text Analysis Techniques

The research analyses trends and variations in discussions related to cohousing and coliving in the X Social Network (formerly known as Twitter) between 2019 and 2022. Employing advanced text network analysis techniques, the research uses Python and Snscrape for text pre-processing, followed by network graph construction and community detection. The study employs Latent Dirichlet Allocation (LDA) models to identify the topics of discussion in tweets and calculates the tf-idf of bigrams within the main thematic clusters. This study evaluates the relative importance of these bigrams as a function of their frequency in the analysed documents. The results reveal a fractal pattern of influence propagation within the X Social Network. Key topics such as coworking spaces, rental flats and urban planning feature prominently in cohousing discussions, demonstrating the multifaceted impact of cohousing models on diverse populations. This research provides essential insight into the intricate landscape of cohousing conversations, highlighting the pivotal role of cohousing models in addressing contemporary challenge