The metaverse has emerged as a new domain within the digital economy, offering decentralized and participatory ecosystems driven by digital assets such as NFTs, tokens, and cryptocurrencies. Platforms like Decentraland, Roblox, and The Sandbox represent varied models of economic governance and user engagement. However, effective economic management strategies in these virtual environments remain underdeveloped. This study explores how the three platforms manage digital economies, focusing on asset governance, community incentives, and risk dynamics. Using an exploratory qualitative approach, data were collected through semi-structured interviews and document analysis, and then thematically analyzed using NVivo software. Results show decentraland emphasizes DAO-based transparency, Roblox relies on centralized monetization, and The Sandbox balances both in a hybrid model. The study concludes that successful economic strategies in the metaverse require integrating governance structures, incentive mechanisms, and social user interaction. These findings offer insights for building more adaptive, inclusive, and sustainable digital economic frameworks in virtual ecosystems.

In recent years, retail investor participation in Southeast Asian capital markets has surged, contributing to increased market volatility and making sentiment analysis a critical factor in understanding price dynamics. This study investigates the relationship between social media sentiment and stock market fluctuations by focusing on Twitter data during periods of market volatility in Indonesia, Thailand, and Malaysia. The objective is to examine how collective investor emotions, as expressed through social media, correlate with daily stock index movements. Employing an exploratory quantitative approach, the study integrates Natural Language Processing (NLP) methods, both lexicon-based tools such as VADER and advanced transformer-based models like BERT and GPT, to classify over 150,000 tweets into positive, negative, and neutral sentiments. Sentiment scores were then aggregated and statistically tested using Pearson correlation with daily stock index returns, specifically the IDX Composite, SET Index, and FTSE Bursa Malaysia. The findings reveal a significant negative correlation between negative sentiment and market returns, particularly in the IDX Composite (r = -0.61, p < 0.05), indicating that pessimistic sentiment is associated with market downturns. Thailand’s SET Index and Malaysia’s FTSE Index showed moderate to weak negative correlations, with r = -0.43 and r = -0.27, respectively. These results highlight the sensitivity of emerging markets to emotionally driven retail behavior. The study concludes that AI-based sentiment analysis offers a valuable early warning tool for market volatility and can complement traditional financial indicators. It recommends developing AI-based sentiment dashboards and enhancing digital financial literacy to mitigate emotional reactivity among retail investors.