Online social networks shape how we access information, form opinions, and engage in public discourse. However, they also pose risks, such as echo chambers and polarization, particularly exacerbated by their instrumental use and the rise of AI-generated content. The vast amount of available data has transformed social science into a more quantitative discipline, with network analysis serving as a key tool to structure social interactions. However, the complexity of these networks makes it challenging to extract meaningful patterns from big data interactions. Statistical methods based on statistical mechanics can filter noise, allowing for unbiased analysis. By combining validated network analysis with natural language processing, we can better understand social patterns in the evolving social media landscape.