Financial Discourse on Social Media: A Topic Modelling Analysis of Personal Finance on X (Twitter)

The rise of social media as a platform for personal finance discussions has transformed how individuals access, share, and engage with financial knowledge. This shift is particularly evident on X (Twitter), where users increasingly turn to microblogs for advice and discussions on saving, cryptocurrency, investing, and managing debt. Despite the growing influence of these platforms, there remains a significant gap in understanding the thematic structure and emotional tone of personal finance conversations online. This study addresses this gap by analysing 37,466 personal finance-related posts from X, offering insights into the key topics and sentiment patterns of finance-related micro-blogs.Building on methodological frameworks from recent machine learning research, Latent Dirichlet Allocation (LDA) topic modelling, augmented by VADER sentiment analysis were implemented to address the core research question: What dominant themes emerge in crowdsourced financial conversations?The analysis identified five dominant themes: cryptocurrency speculation (35.66% prevalence), debt management (15.84%), budgeting (20.63%), making money online (17.38%), and better money spending (10.49%).This research underscores the need for interdisciplinary studies that bridge personal finance, behavioural economics, and digital communication to better understand how social media influences financial decision-making. The findings have practical implications for policymakers, educators, and financial institutions aiming to enhance financial literacy, while also addressing risks associated with unverified advice and promotional content lacking proper disclosure. By mapping the thematic and emotional landscape of personal finance discourse on X, this study provides a foundation for future research into the evolving role of social media in shaping consumer financial behaviour.The growing reliance on social media for financial guidance necessitates further exploration into its impact on individual decision-making processes and broader market behaviours. This study contributes to this emerging field by offering a replicable framework for analysing large-scale social media datasets, highlighting opportunities for improving financial literacy and risks that demand regulatory attention.