Exploring the General and Educational Use of the Metaverse: Public Perspectives, Sentiments, Attitudes, and Discourses
Bibliographic record
Abstract
Abstract This study examines and analyzes the public perspectives, attitudes, sentiments, and discourses regarding the metaverse and its general and educational use. The study explores four research questions and involves the analysis of two datasets containing over 8 million tweets from Twitter (currently called X). The analysis involves text mining, sentiment analysis, and topic modeling techniques and to carry it out different tools are used, such as the National Research Council Canada (NRC) Word-Emotion Association Lexicon (EmoLex), Valence Aware Dictionary for Sentiment Reasoning (VADER), TextBlob, Latent Dirichlet Allocation (LDA), etc. Based on the results, the increase in interest of the public in the metaverse is in line with that of the educational and scientific communities. The public expressed mostly positive attitudes and emotions toward the general and educational use of the metaverse while the negative sentiment percentage was really low. The sentiments and emotions were more intense within the tweets of the educational dataset. The versatility and applicability of the metaverse emerged from the topic analysis from which eight topics arose: digital currencies, virtual environments, gaming, education, immersive learning environments, entertainment, online communities, and industry. The increasing interest in the metaverse, its potentials to enrich education, and the positive attitudes of the public toward its use in education were evident. More intense emotions and sentiments were expressed in the educational dataset which indicates that impulsive decisions may occur and should be anticipated in the educational domain and that the educational community is open to new approaches and supports technology-enhanced learning. Social media arose as an effective medium to communicate the integration of new technologies and innovations in education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".