Public sentiment and thematic evolution in the metaverse: A large-scale computational analysis of Twitter discourse - Samuel Duraivel; Lavanya Rajendran; Srinidhi Vasudevan; Anna Piazza (PLoS ONE, 2026)
This study investigates public perception of the metaverse through a large-scale computational analysis of 52,874 English-language tweets. Leveraging sentiment analysis tools (VADER and RoBERTa) and unsupervised topic modeling (BERTopic), we categorize discourse into four thematic domains: general metaverse discussion, Meta’s Horizon Worlds, metaverse-related cryptocurrency tokens, and virtual social events. Our findings reveal that 43.0% of tweets express positive sentiment, driven by enthusiasm for immersive innovation and digital transformation, while 23.6% convey skepticism, primarily concerning platform reliability, corporate dominance, and privacy. Sentiment surrounding Horizon Worlds reflects a paradox: underlying optimism is overshadowed by user frustration, with negative tweets generating disproportionately high engagement. Analysis of metaverse token discourse indicates robust investor interest, tempered by persistent concerns over market volatility and fraudulent schemes. Topic modeling further uncovers a notable narrative shift from speculative price-focused discussions toward utility-driven use cases. Virtual events (e.g., digital weddings, concerts) elicit the most positive sentiment (51.3%), with users frequently expressing emotional resonance and communal belonging, as visually reinforced by word cloud analysis. This research contributes to the literature on digital adoption and emerging technologies by mapping the evolving social discourse of the metaverse. It offers actionable insights for platform developers, investors, and educators seeking to align innovation with user expectations and provides a predictive lens for forecasting public readiness for the next generation of digital interaction.