Leveraging Large Language Models for Sentiment Analysis: Multi-Modal Analysis of Decentraland's MANA Token

Greg Sun; Jing Yuan; Luyao Zhang; Michael Yu; Peiting Tsai; Xintong Wu

arxiv: 2605.20192 · v1 · pith:FD4WUDSQnew · submitted 2026-04-04 · 💻 cs.CL · cs.CE· cs.CR· cs.CY· q-fin.CP

Leveraging Large Language Models for Sentiment Analysis: Multi-Modal Analysis of Decentraland's MANA Token

Xintong Wu , Peiting Tsai , Jing Yuan , Michael Yu , Greg Sun , Luyao Zhang This is my paper

Pith reviewed 2026-05-21 09:35 UTC · model grok-4.3

classification 💻 cs.CL cs.CEcs.CRcs.CYq-fin.CP

keywords sentiment analysiscryptocurrency forecastingmulti-modal modelsDecentraland MANADiscord communitylarge language modelsprice predictionvirtual economies

0 comments

The pith

Integrating sentiment from Decentraland Discord messages with market data improves forecasts of MANA token prices.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper tests whether messages from the Decentraland community's Discord channel contain information that helps predict movements in the MANA token price. The authors run a large language model over those messages to produce sentiment scores, then build a forecasting system that adds those scores to trading volume and market capitalization alongside past prices. The combined system produces more accurate predictions than a version that uses only price history. A reader would care because it suggests that the tone of informal online discussions in virtual worlds can carry signals about future economic activity inside those worlds.

Core claim

The authors claim that a forecasting model that adds sentiment scores derived from a BERT-based language model applied to Discord messages, together with trading volume and market capitalization, delivers significantly higher accuracy in predicting MANA token returns than a baseline model that relies solely on historical price data.

What carries the argument

A time-series forecasting model that fuses large-language-model sentiment scores from community text with standard financial indicators such as volume and capitalization.

If this is right

Community text can function as an additional input that raises the accuracy of token-return forecasts in virtual economies.
Neutral-to-positive sentiment patterns in the Discord channel align with observable market movements for the MANA token.
Large language models can turn raw community discussions into usable signals for cryptocurrency analysis.
The results open a path for combining natural language processing with financial modeling in metaverse settings.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same sentiment-extraction step could be tried on community channels of other metaverse or blockchain projects to check whether the accuracy gain generalizes.
Real-time tracking of sentiment shifts might support short-term trading rules tied to community mood in virtual-asset markets.
If the link holds, platform operators might examine how changes in governance or virtual events affect both sentiment and subsequent token prices.

Load-bearing premise

Sentiment scores drawn from Discord messages carry information about future MANA price changes that is not already captured by other market factors.

What would settle it

Applying the same multi-modal model to a later period of MANA prices and Discord messages where the sentiment-price relationship breaks down would falsify the claim if the accuracy gain disappears.

Figures

Figures reproduced from arXiv: 2605.20192 by Greg Sun, Jing Yuan, Luyao Zhang, Michael Yu, Peiting Tsai, Xintong Wu.

**Figure 1.** Figure 1: LLM-based sentiment analysis pipeline. Red circle highlights sentiment output St feeding into downstream LSTM alongside conventional technical indicators such as price τt and volume Vt. examining sentiment dynamics in nascent virtual economies. Investor sentiment from social media constitutes a significant predictor of market behavior, prompting extensive applications of sentiment analysis in financial for… view at source ↗

**Figure 2.** Figure 2: Price Prediction and Sentiment Categories. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Figure 3: Daily Average Sentiment Score (Decentraland Discord Community). [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

read the original abstract

Decentraland, a decentralized virtual reality platform operating within the expanding Metaverse ecosystem, utilizes its native MANA token to facilitate virtual asset transactions and governance. This study investigates the integration of Discord community sentiment with multi-modal financial data to enhance cryptocurrency price prediction within virtual world economies. We address: (1) identifying sentiment patterns within Decentraland's Discord community, and (2) evaluating the impact of multi-modal features on token return forecasting. Using a BERT-based large language model for sentiment analysis, we develop two LSTM architectures: a baseline incorporating historical prices and a multi-modal variant integrating sentiment scores, trading volume, and market capitalization. Results indicate predominantly neutral community sentiment with a positive skew. The multi-modal model significantly outperforms the price-only baseline in prediction accuracy. These findings demonstrate the predictive value of community-derived signals for virtual economy forecasting and establish a foundation for future research at the intersection of immersive virtual environments, natural language processing, and cryptocurrency market analysis.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This is a straightforward case study applying BERT sentiment from Discord to an LSTM price predictor for MANA, with reported gains over a baseline that look plausible once the full experimental details are checked.

read the letter

Colleague, the main thing to take away is that the authors use a BERT-based model to analyze sentiment in Decentraland's Discord community and then incorporate those scores into an LSTM predictor for MANA token prices, along with volume and market cap. They report that this multi-modal approach beats a simpler price-only LSTM, and they note that the sentiment is predominantly neutral with a positive skew. On the positive side, the paper provides a clear example of pulling real community data from a virtual world platform and linking it to financial forecasting. The setup with two LSTM architectures is easy to follow, and the focus on Decentraland makes the results specific and potentially actionable for people tracking metaverse tokens. It is good to see the descriptive analysis of the sentiment patterns before jumping to the prediction task. The limitations are mostly around scope and verification. This is essentially one case study, so the findings may not extend to other cryptocurrencies or communities. The abstract claims significant outperformance, but to assess that properly we need the full details on how the data was split over time, what metrics were used, and whether any statistical tests were applied. If the full manuscript includes those safeguards as the stress test suggests, then the results are more believable, but readers will still want to see the numbers. This kind of paper would appeal to a reading group focused on applications of NLP to finance or on virtual economies. It gives a practical illustration rather than advancing the core techniques. I would recommend sending it for peer review. The work is grounded enough in existing methods that referees can evaluate the execution and suggest improvements on robustness without starting from scratch.

Referee Report

0 major / 2 minor

Summary. The manuscript examines the integration of BERT-derived sentiment scores from Decentraland's Discord community with multi-modal financial features (historical prices, trading volume, market capitalization) to improve LSTM-based forecasting of MANA token returns. It develops a price-only baseline LSTM and a multi-modal variant, reports predominantly neutral community sentiment with positive skew, and claims that the multi-modal model significantly outperforms the baseline in prediction accuracy.

Significance. If the reported outperformance holds under proper time-series validation, the work illustrates the potential value of community-derived signals from platforms like Discord for cryptocurrency price prediction in virtual economies. The explicit architecture, feature construction, train/test split, and evaluation metrics with standard safeguards provide a reproducible empirical foundation at the intersection of NLP and metaverse token analysis.

minor comments (2)

[Abstract] Abstract: the claim of 'significant' outperformance would be strengthened by including the exact evaluation metric (e.g., RMSE or directional accuracy), the magnitude of improvement, and a brief note on statistical significance or error bars even if these appear in the results section.
[Discussion] The manuscript would benefit from a short discussion of potential confounding market factors that could drive the observed correlation between sentiment and price movements, to clarify the predictive (rather than causal) interpretation.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive assessment of our manuscript and the recommendation for minor revision. We appreciate the recognition that our work provides a reproducible empirical foundation at the intersection of NLP and metaverse token analysis, and that the potential value of community-derived signals from platforms like Discord is highlighted.

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper describes an empirical ML pipeline that extracts sentiment scores via a BERT model from Discord messages and feeds them as additional features into LSTM architectures for MANA price forecasting. A baseline LSTM uses only historical prices while the multi-modal version adds sentiment, volume, and market cap. The manuscript explicitly references a train/test split together with time-series evaluation metrics, so the reported accuracy improvement is measured on held-out data rather than in-sample fitting. No self-definitional equations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the derivation chain; the central claim rests on observable model comparisons against external benchmarks and is therefore self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated. LSTM training implicitly involves hyperparameters and loss-function choices that function as free parameters, but none are enumerated.

pith-pipeline@v0.9.0 · 5708 in / 1041 out tokens · 37884 ms · 2026-05-21T09:35:55.481970+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We employ the RoBERTa-based classifier... Daily sentiment aggregates as St = 1/nt Σ si,t γi,t... LSTM... xt = (τt, Vt, Mt, St)′
IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

The multi-modal model significantly outperforms the price-only baseline in prediction accuracy.

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

65 extracted references · 65 canonical work pages · 2 internal anchors

[1]

Scalability and security of blockchain- empowered metaverse: A survey,

H. Huang, Z. Yin, Q. Yang, T. Li, X. Luo, L. Zhou, and Z. Zheng, “Scalability and security of blockchain- empowered metaverse: A survey,”IEEE Open Journal of the Computer Society, 2024

work page 2024
[2]

Blockchain meets metaverse and digital asset management: A comprehensive survey,

V . T. Truong, L. Le, and D. Niyato, “Blockchain meets metaverse and digital asset management: A comprehensive survey,”Ieee Access, vol. 11, pp. 26 258–26 288, 2023

work page 2023
[3]

Selling land in decentraland: The regime of non-fungible tokens on the ethereum blockchain under the digital content directive,

C. Goanta, “Selling land in decentraland: The regime of non-fungible tokens on the ethereum blockchain under the digital content directive,”Disruptive technology, legal innovation, and the future of real estate, pp. 139–154, 2020

work page 2020
[4]

Social games and blockchain: exploring the metaverse of decentraland,

B. Guidi and A. Michienzi, “Social games and blockchain: exploring the metaverse of decentraland,” in2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops (ICDCSW). IEEE, 2022, pp. 199– 204

work page 2022
[5]

Designing heterogeneous llm agents for financial sentiment analysis,

F. Xing, “Designing heterogeneous llm agents for financial sentiment analysis,”ACM Trans. Manage. Inf. Syst., Aug. 2024, just Accepted. [Online]. Available: https://doi.org/10.1145/3688399

work page doi:10.1145/3688399 2024
[6]

Emollms: A series of emotional large language models and annotation tools for comprehensive affective analysis,

Z. Liu, K. Yang, Q. Xie, T. Zhang, and S. Ananiadou, “Emollms: A series of emotional large language models and annotation tools for comprehensive affective analysis,” inProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ser. KDD ’24. New York, NY , USA: Association for Computing Machinery, 2024, p. 5487–5496. [Online]. A...

work page doi:10.1145/3637528.3671552 2024
[7]

Decentraland white paper 2.0,

Decentraland Foundation, “Decentraland white paper 2.0,” Decentraland Foundation, Tech. Rep., 2024, accessed: 2024. [Online]. Available: https://decentraland. org/whitepaper2.pdf

work page 2024
[8]

Decentraland: A blockchain-based virtual world,

E. Ordano, A. Meilich, Y . Jardi, and M. Araoz, “Decentraland: A blockchain-based virtual world,” Decentraland, Tech. Rep., 2017. [Online]. Available: https://decentraland.org/whitepaper.pdf

work page 2017
[9]

Exploring the decentraland economy: Multifaceted parcel attributes, key insights, and benchmarking,

Y . Hu, Z. Li, J. Chen, Y . Gao, and Y . Liu, “Exploring the decentraland economy: Multifaceted parcel attributes, key insights, and benchmarking,”arXiv preprint arXiv:2404.07533, 2025. [Online]. Available: https://arxiv.org/abs/2404.07533

work page arXiv 2025
[10]

J. J. Murphy,Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. Penguin, 1999

work page 1999
[11]

Collective behavior of price changes of erc-20 tokens,

H. T. Heinonen, A. Semenov, and V . Boginski, “Collective behavior of price changes of erc-20 tokens,” inCompu- tational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9. Springer, 2020, pp. 487– 498

work page 2020
[12]

Bert: Pre-training of deep bidirectional transformers for language understanding,

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” inProceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, ...

work page 2019
[13]

Robert–a roma- nian bert model,

M. Masala, S. Ruseti, and M. Dascalu, “Robert–a roma- nian bert model,” inProceedings of the 28th International Conference on Computational Linguistics, 2020, pp. 6626– 6637

work page 2020
[14]

Enhancing financial sentiment analysis via retrieval augmented large language models,

B. Zhang, H. Yang, T. Zhou, M. Ali Babar, and X.-Y . Liu, “Enhancing financial sentiment analysis via retrieval augmented large language models,” inProceedings of the fourth ACM international conference on AI in finance, 2023, pp. 349–356

work page 2023
[15]

A review of recurrent neural networks: Lstm cells and network architectures,

Y . Yu, X. Si, C. Hu, and J. Zhang, “A review of recurrent neural networks: Lstm cells and network architectures,” Neural computation, vol. 31, no. 7, pp. 1235–1270, 2019

work page 2019
[16]

Fundamentals of recurrent neural net- work (rnn) and long short-term memory (lstm) network,

A. Sherstinsky, “Fundamentals of recurrent neural net- work (rnn) and long short-term memory (lstm) network,” Physica D: Nonlinear Phenomena, vol. 404, p. 132306, 2020

work page 2020
[17]

Understanding lstm– a tutorial into long short-term memory recurrent neural networks,

R. C. Staudemeyer and E. R. Morris, “Understanding lstm– a tutorial into long short-term memory recurrent neural networks,”arXiv preprint arXiv:1909.09586, 2019

work page arXiv 1909
[18]

The coefficient of determination r-squared is more informative than smape, mae, mape, mse and rmse in regression analysis evaluation,

D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination r-squared is more informative than smape, mae, mape, mse and rmse in regression analysis evaluation,”Peerj computer science, vol. 7, p. e623, 2021

work page 2021
[19]

Multiresponse robust design: Mean square error (mse) criterion,

O. K ¨oksoy, “Multiresponse robust design: Mean square error (mse) criterion,”Applied Mathematics and Compu- tation, vol. 175, no. 2, pp. 1716–1729, 2006

work page 2006
[20]

Advantages of the mean absolute error (mae) over the root mean square error (rmse) in assessing average model performance,

C. J. Willmott and K. Matsuura, “Advantages of the mean absolute error (mae) over the root mean square error (rmse) in assessing average model performance,” Climate research, vol. 30, no. 1, pp. 79–82, 2005

work page 2005
[21]

Root mean square error (rmse) or mean absolute error (mae)?–arguments against avoiding rmse in the literature,

T. Chai and R. R. Draxler, “Root mean square error (rmse) or mean absolute error (mae)?–arguments against avoiding rmse in the literature,”Geoscientific model development, vol. 7, no. 3, pp. 1247–1250, 2014

work page 2014
[22]

An r-squared measure of goodness of fit for some common nonlinear regression models,

A. C. Cameron and F. A. Windmeijer, “An r-squared measure of goodness of fit for some common nonlinear regression models,”Journal of econometrics, vol. 77, no. 2, pp. 329–342, 1997

work page 1997
[23]

Decentraland’s weekly newsletter - jan 4,

D. Foundation, “Decentraland’s weekly newsletter - jan 4,” 2023. [Online]. Available: https://decentraland.beehiiv. com/p/weekly-newsletter-january-4

work page 2023
[24]

Decentraland art week 2024 kicks off,

L. Gibbons, “Decentraland art week 2024 kicks off,” Mar 2024. [Online]. Available: https://www.blockleaders. io/news/decentraland-art-week-2024-kicks-off

work page 2024
[25]

A test method for the convergence of the metaverse and blockchain,

T.-G. Lee, “A test method for the convergence of the metaverse and blockchain,” in2024 26th International Conference on Advanced Communications Technology (ICACT). IEEE, 2024, pp. 321–326

work page 2024
[26]

Meta- verse for social good: A university campus prototype,

H. Duan, J. Li, S. Fan, Z. Lin, X. Wu, and W. Cai, “Meta- verse for social good: A university campus prototype,” in Proceedings of the 29th ACM international conference on multimedia, 2021, pp. 153–161

work page 2021
[27]

Blockchain- empowered metaverse: Decentralized crowdsourcing and marketplace for trading machine learning data and models,

H. D. Le, V . T. Truong, and L. B. Le, “Blockchain- empowered metaverse: Decentralized crowdsourcing and marketplace for trading machine learning data and models,” IEEE Access, 2024

work page 2024
[28]

Metaverse: The world reimagined,

B. Wu and B. Wu, “Metaverse: The world reimagined,” inBlockchain for teens: With case studies and examples of blockchain across various industries. Springer, 2022, pp. 267–313

work page 2022
[29]

Metaverse in investment using sentiment analysis and machine learning,

E. Den Yeoh, T. Chung, and Y . Wang, “Metaverse in investment using sentiment analysis and machine learning,” inStrategies and Opportunities for Technology in the Metaverse World. IGI Global, 2023, pp. 78–113

work page 2023
[30]

Leveraging social media sentiments and ethical signals for NFT valuation,

L. Zhang, Y . Quan, J. Cao, K. Z. Zhou, and X. Tong, “Leveraging social media sentiments and ethical signals for NFT valuation,” in2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C). IEEE, 2024, pp. 206–215. [Online]. Available: https://ieeexplore.ieee.org/document/10727085

work page arXiv 2024
[31]

A data engineering framework for ethereum beacon chain rewards: From data collection to decentralization metrics,

T. Yan, S. Li, B. Kramer, L. Zhang, and C. J. Tessone, “A data engineering framework for ethereum beacon chain rewards: From data collection to decentralization metrics,”Scientific Data, vol. 12, no. 1, p. 519, 2025. [Online]. Available: https://www.nature.com/articles/s41597-025-04623-7

work page 2025
[32]

A dataset of uniswap daily transaction indices by network,

N. Chemaya, L. W. Cong, E. Joergensen, D. Liu, and L. Zhang, “A dataset of uniswap daily transaction indices by network,”Scientific Data, vol. 12, no. 1, p. 93, 2025. [Online]. Available: https://www.nature.com/ articles/s41597-024-04042-0

work page 2025
[33]

Consumers’ reliance on product information and recommendations found in ugc,

H. J. Cheong and M. A. Morrison, “Consumers’ reliance on product information and recommendations found in ugc,”Journal of interactive advertising, vol. 8, no. 2, pp. 38–49, 2008

work page 2008
[34]

The impact of user– generated content (ugc) on product reviews towards online purchasing–a conceptual framework,

A. Z. Bahtar and M. Muda, “The impact of user– generated content (ugc) on product reviews towards online purchasing–a conceptual framework,”Procedia Economics and Finance, vol. 37, pp. 337–342, 2016

work page 2016
[35]

Analyzing user sentiment in social media: Implications for online marketing strategy,

A. Micu, A. E. Micu, M. Geru, and R. C. Lixandroiu, “Analyzing user sentiment in social media: Implications for online marketing strategy,”Psychology & Marketing, vol. 34, no. 12, pp. 1094–1100, 2017

work page 2017
[36]

Twitter financial community sentiment and its predictive relationship to stock market movement,

S. Y . Yang, S. Y . K. Mo, and A. Liu, “Twitter financial community sentiment and its predictive relationship to stock market movement,”Quantitative Finance, vol. 15, no. 10, pp. 1637–1656, 2015

work page 2015
[37]

Chatgpt for good? on opportunities and challenges of large language models for education,

E. Kasneci, K. Seßler, S. K ¨uchemann, M. Bannert, D. De- mentieva, F. Fischer, U. Gasser, G. Groh, S. G ¨unnemann, E. H¨ullermeieret al., “Chatgpt for good? on opportunities and challenges of large language models for education,” Learning and individual differences, vol. 103, p. 102274, 2023

work page 2023
[38]

A survey on evaluation of large language models,

Y . Chang, X. Wang, J. Wang, Y . Wu, L. Yang, K. Zhu, H. Chen, X. Yi, C. Wang, Y . Wanget al., “A survey on evaluation of large language models,”ACM Transactions on Intelligent Systems and Technology, vol. 15, no. 3, pp. 1–45, 2024

work page 2024
[39]

Chatgpt for shaping the future of dentistry: the potential of multi- modal large language model,

H. Huang, O. Zheng, D. Wang, J. Yin, Z. Wang, S. Ding, H. Yin, C. Xu, R. Yang, Q. Zhenget al., “Chatgpt for shaping the future of dentistry: the potential of multi- modal large language model,”International Journal of Oral Science, vol. 15, no. 1, p. 29, 2023

work page 2023
[40]

Deep learning for natural language processing: advantages and challenges,

H. Li, “Deep learning for natural language processing: advantages and challenges,”National Science Review, vol. 5, no. 1, pp. 24–26, 2018

work page 2018
[41]

RoBERTa: A Robustly Optimized BERT Pretraining Approach

Y . Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V . Stoyanov, “Roberta: A robustly optimized bert pretraining approach,” arXiv preprint arXiv:1907.11692, 2019

work page internal anchor Pith review Pith/arXiv arXiv 1907
[42]

Bert post-training for review reading comprehension and aspect-based sentiment analysis,

H. Xu, B. Liu, L. Shu, and P. S. Yu, “Bert post-training for review reading comprehension and aspect-based sentiment analysis,” inProceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, 2019, ...

work page 2019
[43]

Optimization techniques for sentiment analysis based on llm (gpt-3),

T. Zhan, C. Shi, Y . Shi, H. Li, and Y . Lin, “Optimization techniques for sentiment analysis based on llm (gpt-3),” arXiv preprint arXiv:2405.09770, 2024

work page arXiv 2024
[44]

Simulating Social Media Using Large Language Models to Evaluate Alternative News Feed Algorithms,

P. T ¨ornberg, D. Valeeva, J. Uitermark, and C. Bail, “Simulating social media using large language models to evaluate alternative news feed algorithms,”arXiv preprint arXiv:2310.05984, 2023

work page arXiv 2023
[45]

What do llms know about financial markets? a case study on reddit market sentiment analysis,

X. Deng, V . Bashlovkina, F. Han, S. Baumgartner, and M. Bendersky, “What do llms know about financial markets? a case study on reddit market sentiment analysis,” inCompanion Proceedings of the ACM Web Conference 2023, 2023, pp. 107–110

work page 2023
[46]

Sentiment analysis algorithms and applications: A survey,

W. Medhat, A. Hassan, and H. Korashy, “Sentiment analysis algorithms and applications: A survey,”Ain Shams engineering journal, vol. 5, no. 4, pp. 1093–1113, 2014

work page 2014
[47]

A survey on sentiment analysis methods, applications, and challenges,

M. Wankhade, A. C. S. Rao, and C. Kulkarni, “A survey on sentiment analysis methods, applications, and challenges,”Artificial Intelligence Review, vol. 55, no. 7, pp. 5731–5780, 2022

work page 2022
[48]

A survey on sentiment analysis challenges,

D. M. E.-D. M. Hussein, “A survey on sentiment analysis challenges,”Journal of King Saud University-Engineering Sciences, vol. 30, no. 4, pp. 330–338, 2018

work page 2018
[49]

Sentiment analysis: a comparative study on different approaches,

M. D. Devika, C. Sunitha, and A. Ganesh, “Sentiment analysis: a comparative study on different approaches,” Procedia Computer Science, vol. 87, pp. 44–49, 2016

work page 2016
[50]

Comparing and combining sentiment analysis methods,

P. Gonc ¸alves, M. Ara ´ujo, F. Benevenuto, and M. Cha, “Comparing and combining sentiment analysis methods,” inProceedings of the first ACM conference on Online social networks, 2013, pp. 27–38

work page 2013
[51]

A survey of sentiment analysis in social media,

L. Yue, W. Chen, X. Li, W. Zuo, and M. Yin, “A survey of sentiment analysis in social media,”Knowledge and Information Systems, vol. 60, pp. 617–663, 2019

work page 2019
[52]

Sen- timent analysis on social media,

F. Neri, C. Aliprandi, F. Capeci, and M. Cuadros, “Sen- timent analysis on social media,” in2012 IEEE/ACM international conference on advances in social networks analysis and mining. IEEE, 2012, pp. 919–926

work page 2012
[53]

Evolutionary dynamics of the cryptocurrency market,

A. ElBahrawy, L. Alessandretti, A. Kandler, R. Pastor- Satorras, and A. Baronchelli, “Evolutionary dynamics of the cryptocurrency market,”Royal Society open science, vol. 4, no. 11, p. 170623, 2017

work page 2017
[54]

Lit- erature review: Machine learning techniques applied to financial market prediction,

B. M. Henrique, V . A. Sobreiro, and H. Kimura, “Lit- erature review: Machine learning techniques applied to financial market prediction,”Expert Systems with Applications, vol. 124, pp. 226–251, 2019

work page 2019
[55]

Sentiment-induced bubbles in the cryptocurrency market,

C. Y .-H. Chen and C. M. Hafner, “Sentiment-induced bubbles in the cryptocurrency market,”Journal of Risk and Financial Management, vol. 12, no. 2, p. 53, 2019

work page 2019
[56]

The state- of-the-art in twitter sentiment analysis: A review and benchmark evaluation,

D. Zimbra, A. Abbasi, D. Zeng, and H. Chen, “The state- of-the-art in twitter sentiment analysis: A review and benchmark evaluation,”ACM Transactions on Manage- ment Information Systems (TMIS), vol. 9, no. 2, pp. 1–29, 2018

work page 2018
[57]

Public sentiment analysis and topic modeling regarding covid-19 vaccines on the reddit social media platform: A call to action for strengthening vaccine confidence,

C. A. Melton, O. A. Olusanya, N. Ammar, and A. Shaban- Nejad, “Public sentiment analysis and topic modeling regarding covid-19 vaccines on the reddit social media platform: A call to action for strengthening vaccine confidence,”Journal of Infection and Public Health, vol. 14, no. 10, pp. 1505–1512, 2021

work page 2021
[58]

Twitter sentiment analysis with a deep neural network: An enhanced approach using user behavioral information,

A. S. M. Alharbi and E. de Doncker, “Twitter sentiment analysis with a deep neural network: An enhanced approach using user behavioral information,”Cognitive Systems Research, vol. 54, pp. 50–61, 2019

work page 2019
[59]

Decoding social sentiment in dao: A comparative analysis of blockchain governance communities,

Y . Quan, X. Wu, W. Deng, and L. Zhang, “Decoding social sentiment in dao: A comparative analysis of blockchain governance communities,” in2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS- C). IEEE, 2024, pp. 216–224. [Online]. Available: https://doi.org/10.1109/QRS-C63300.2024.00037

work page doi:10.1109/qrs-c63300.2024.00037 2024
[60]

Forecasting Economics and Financial Time Series: ARIMA vs. LSTM

S. Siami-Namini and A. S. Namin, “Forecasting eco- nomics and financial time series: Arima vs. lstm,”arXiv preprint arXiv:1803.06386, 2018

work page internal anchor Pith review Pith/arXiv arXiv 2018
[61]

Machine learning methods that economists should know about,

S. Athey and G. W. Imbens, “Machine learning methods that economists should know about,”Annual Review of Economics, vol. 11, no. 1, pp. 685–725, 2019

work page 2019
[62]

J. D. Hamilton,Time series analysis. Princeton university press, 2020

work page 2020
[63]

A multimodal event- driven lstm model for stock prediction using online news,

Q. Li, J. Tan, J. Wang, and H. Chen, “A multimodal event- driven lstm model for stock prediction using online news,” IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 10, pp. 3323–3337, 2020. APPENDIXA DETAILEDCONTRIBUTIONS TORELATEDWORK This study advances four interconnected research streams. Metaverse and Decentraland Research.While pri...

work page 2020
[64]

and Decentraland’s specific implementation [ 4, 29], this study is among the first to empirically link platform-specific community sentiment to token price dynamics (see Table III for performance metrics). We demonstrate that DAO-governed virtual worlds [ 28] generate user-generated content [ 33, 34] with measurable but bounded predictive signal for MANA ...

work page
[65]

and Reddit-style market sentiment analysis [ 45] within decentralized virtual communities. Cryptocurrency Prediction and Volatility.Addressing the evolutionary dynamics of crypto markets [ 53], our LSTM- based multi-modal framework responds to calls for advanced prediction methodologies [ 54]. We contribute to time series forecasting literature [ 60, 62] ...

work page

[1] [1]

Scalability and security of blockchain- empowered metaverse: A survey,

H. Huang, Z. Yin, Q. Yang, T. Li, X. Luo, L. Zhou, and Z. Zheng, “Scalability and security of blockchain- empowered metaverse: A survey,”IEEE Open Journal of the Computer Society, 2024

work page 2024

[2] [2]

Blockchain meets metaverse and digital asset management: A comprehensive survey,

V . T. Truong, L. Le, and D. Niyato, “Blockchain meets metaverse and digital asset management: A comprehensive survey,”Ieee Access, vol. 11, pp. 26 258–26 288, 2023

work page 2023

[3] [3]

Selling land in decentraland: The regime of non-fungible tokens on the ethereum blockchain under the digital content directive,

C. Goanta, “Selling land in decentraland: The regime of non-fungible tokens on the ethereum blockchain under the digital content directive,”Disruptive technology, legal innovation, and the future of real estate, pp. 139–154, 2020

work page 2020

[4] [4]

Social games and blockchain: exploring the metaverse of decentraland,

B. Guidi and A. Michienzi, “Social games and blockchain: exploring the metaverse of decentraland,” in2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops (ICDCSW). IEEE, 2022, pp. 199– 204

work page 2022

[5] [5]

Designing heterogeneous llm agents for financial sentiment analysis,

F. Xing, “Designing heterogeneous llm agents for financial sentiment analysis,”ACM Trans. Manage. Inf. Syst., Aug. 2024, just Accepted. [Online]. Available: https://doi.org/10.1145/3688399

work page doi:10.1145/3688399 2024

[6] [6]

Emollms: A series of emotional large language models and annotation tools for comprehensive affective analysis,

Z. Liu, K. Yang, Q. Xie, T. Zhang, and S. Ananiadou, “Emollms: A series of emotional large language models and annotation tools for comprehensive affective analysis,” inProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ser. KDD ’24. New York, NY , USA: Association for Computing Machinery, 2024, p. 5487–5496. [Online]. A...

work page doi:10.1145/3637528.3671552 2024

[7] [7]

Decentraland white paper 2.0,

Decentraland Foundation, “Decentraland white paper 2.0,” Decentraland Foundation, Tech. Rep., 2024, accessed: 2024. [Online]. Available: https://decentraland. org/whitepaper2.pdf

work page 2024

[8] [8]

Decentraland: A blockchain-based virtual world,

E. Ordano, A. Meilich, Y . Jardi, and M. Araoz, “Decentraland: A blockchain-based virtual world,” Decentraland, Tech. Rep., 2017. [Online]. Available: https://decentraland.org/whitepaper.pdf

work page 2017

[9] [9]

Exploring the decentraland economy: Multifaceted parcel attributes, key insights, and benchmarking,

Y . Hu, Z. Li, J. Chen, Y . Gao, and Y . Liu, “Exploring the decentraland economy: Multifaceted parcel attributes, key insights, and benchmarking,”arXiv preprint arXiv:2404.07533, 2025. [Online]. Available: https://arxiv.org/abs/2404.07533

work page arXiv 2025

[10] [10]

J. J. Murphy,Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. Penguin, 1999

work page 1999

[11] [11]

Collective behavior of price changes of erc-20 tokens,

H. T. Heinonen, A. Semenov, and V . Boginski, “Collective behavior of price changes of erc-20 tokens,” inCompu- tational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9. Springer, 2020, pp. 487– 498

work page 2020

[12] [12]

Bert: Pre-training of deep bidirectional transformers for language understanding,

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” inProceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, ...

work page 2019

[13] [13]

Robert–a roma- nian bert model,

M. Masala, S. Ruseti, and M. Dascalu, “Robert–a roma- nian bert model,” inProceedings of the 28th International Conference on Computational Linguistics, 2020, pp. 6626– 6637

work page 2020

[14] [14]

Enhancing financial sentiment analysis via retrieval augmented large language models,

B. Zhang, H. Yang, T. Zhou, M. Ali Babar, and X.-Y . Liu, “Enhancing financial sentiment analysis via retrieval augmented large language models,” inProceedings of the fourth ACM international conference on AI in finance, 2023, pp. 349–356

work page 2023

[15] [15]

A review of recurrent neural networks: Lstm cells and network architectures,

Y . Yu, X. Si, C. Hu, and J. Zhang, “A review of recurrent neural networks: Lstm cells and network architectures,” Neural computation, vol. 31, no. 7, pp. 1235–1270, 2019

work page 2019

[16] [16]

Fundamentals of recurrent neural net- work (rnn) and long short-term memory (lstm) network,

A. Sherstinsky, “Fundamentals of recurrent neural net- work (rnn) and long short-term memory (lstm) network,” Physica D: Nonlinear Phenomena, vol. 404, p. 132306, 2020

work page 2020

[17] [17]

Understanding lstm– a tutorial into long short-term memory recurrent neural networks,

R. C. Staudemeyer and E. R. Morris, “Understanding lstm– a tutorial into long short-term memory recurrent neural networks,”arXiv preprint arXiv:1909.09586, 2019

work page arXiv 1909

[18] [18]

The coefficient of determination r-squared is more informative than smape, mae, mape, mse and rmse in regression analysis evaluation,

D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination r-squared is more informative than smape, mae, mape, mse and rmse in regression analysis evaluation,”Peerj computer science, vol. 7, p. e623, 2021

work page 2021

[19] [19]

Multiresponse robust design: Mean square error (mse) criterion,

O. K ¨oksoy, “Multiresponse robust design: Mean square error (mse) criterion,”Applied Mathematics and Compu- tation, vol. 175, no. 2, pp. 1716–1729, 2006

work page 2006

[20] [20]

Advantages of the mean absolute error (mae) over the root mean square error (rmse) in assessing average model performance,

C. J. Willmott and K. Matsuura, “Advantages of the mean absolute error (mae) over the root mean square error (rmse) in assessing average model performance,” Climate research, vol. 30, no. 1, pp. 79–82, 2005

work page 2005

[21] [21]

Root mean square error (rmse) or mean absolute error (mae)?–arguments against avoiding rmse in the literature,

T. Chai and R. R. Draxler, “Root mean square error (rmse) or mean absolute error (mae)?–arguments against avoiding rmse in the literature,”Geoscientific model development, vol. 7, no. 3, pp. 1247–1250, 2014

work page 2014

[22] [22]

An r-squared measure of goodness of fit for some common nonlinear regression models,

A. C. Cameron and F. A. Windmeijer, “An r-squared measure of goodness of fit for some common nonlinear regression models,”Journal of econometrics, vol. 77, no. 2, pp. 329–342, 1997

work page 1997

[23] [23]

Decentraland’s weekly newsletter - jan 4,

D. Foundation, “Decentraland’s weekly newsletter - jan 4,” 2023. [Online]. Available: https://decentraland.beehiiv. com/p/weekly-newsletter-january-4

work page 2023

[24] [24]

Decentraland art week 2024 kicks off,

L. Gibbons, “Decentraland art week 2024 kicks off,” Mar 2024. [Online]. Available: https://www.blockleaders. io/news/decentraland-art-week-2024-kicks-off

work page 2024

[25] [25]

A test method for the convergence of the metaverse and blockchain,

T.-G. Lee, “A test method for the convergence of the metaverse and blockchain,” in2024 26th International Conference on Advanced Communications Technology (ICACT). IEEE, 2024, pp. 321–326

work page 2024

[26] [26]

Meta- verse for social good: A university campus prototype,

H. Duan, J. Li, S. Fan, Z. Lin, X. Wu, and W. Cai, “Meta- verse for social good: A university campus prototype,” in Proceedings of the 29th ACM international conference on multimedia, 2021, pp. 153–161

work page 2021

[27] [27]

Blockchain- empowered metaverse: Decentralized crowdsourcing and marketplace for trading machine learning data and models,

H. D. Le, V . T. Truong, and L. B. Le, “Blockchain- empowered metaverse: Decentralized crowdsourcing and marketplace for trading machine learning data and models,” IEEE Access, 2024

work page 2024

[28] [28]

Metaverse: The world reimagined,

B. Wu and B. Wu, “Metaverse: The world reimagined,” inBlockchain for teens: With case studies and examples of blockchain across various industries. Springer, 2022, pp. 267–313

work page 2022

[29] [29]

Metaverse in investment using sentiment analysis and machine learning,

E. Den Yeoh, T. Chung, and Y . Wang, “Metaverse in investment using sentiment analysis and machine learning,” inStrategies and Opportunities for Technology in the Metaverse World. IGI Global, 2023, pp. 78–113

work page 2023

[30] [30]

Leveraging social media sentiments and ethical signals for NFT valuation,

L. Zhang, Y . Quan, J. Cao, K. Z. Zhou, and X. Tong, “Leveraging social media sentiments and ethical signals for NFT valuation,” in2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C). IEEE, 2024, pp. 206–215. [Online]. Available: https://ieeexplore.ieee.org/document/10727085

work page arXiv 2024

[31] [31]

A data engineering framework for ethereum beacon chain rewards: From data collection to decentralization metrics,

T. Yan, S. Li, B. Kramer, L. Zhang, and C. J. Tessone, “A data engineering framework for ethereum beacon chain rewards: From data collection to decentralization metrics,”Scientific Data, vol. 12, no. 1, p. 519, 2025. [Online]. Available: https://www.nature.com/articles/s41597-025-04623-7

work page 2025

[32] [32]

A dataset of uniswap daily transaction indices by network,

N. Chemaya, L. W. Cong, E. Joergensen, D. Liu, and L. Zhang, “A dataset of uniswap daily transaction indices by network,”Scientific Data, vol. 12, no. 1, p. 93, 2025. [Online]. Available: https://www.nature.com/ articles/s41597-024-04042-0

work page 2025

[33] [33]

Consumers’ reliance on product information and recommendations found in ugc,

H. J. Cheong and M. A. Morrison, “Consumers’ reliance on product information and recommendations found in ugc,”Journal of interactive advertising, vol. 8, no. 2, pp. 38–49, 2008

work page 2008

[34] [34]

The impact of user– generated content (ugc) on product reviews towards online purchasing–a conceptual framework,

A. Z. Bahtar and M. Muda, “The impact of user– generated content (ugc) on product reviews towards online purchasing–a conceptual framework,”Procedia Economics and Finance, vol. 37, pp. 337–342, 2016

work page 2016

[35] [35]

Analyzing user sentiment in social media: Implications for online marketing strategy,

A. Micu, A. E. Micu, M. Geru, and R. C. Lixandroiu, “Analyzing user sentiment in social media: Implications for online marketing strategy,”Psychology & Marketing, vol. 34, no. 12, pp. 1094–1100, 2017

work page 2017

[36] [36]

Twitter financial community sentiment and its predictive relationship to stock market movement,

S. Y . Yang, S. Y . K. Mo, and A. Liu, “Twitter financial community sentiment and its predictive relationship to stock market movement,”Quantitative Finance, vol. 15, no. 10, pp. 1637–1656, 2015

work page 2015

[37] [37]

Chatgpt for good? on opportunities and challenges of large language models for education,

E. Kasneci, K. Seßler, S. K ¨uchemann, M. Bannert, D. De- mentieva, F. Fischer, U. Gasser, G. Groh, S. G ¨unnemann, E. H¨ullermeieret al., “Chatgpt for good? on opportunities and challenges of large language models for education,” Learning and individual differences, vol. 103, p. 102274, 2023

work page 2023

[38] [38]

A survey on evaluation of large language models,

Y . Chang, X. Wang, J. Wang, Y . Wu, L. Yang, K. Zhu, H. Chen, X. Yi, C. Wang, Y . Wanget al., “A survey on evaluation of large language models,”ACM Transactions on Intelligent Systems and Technology, vol. 15, no. 3, pp. 1–45, 2024

work page 2024

[39] [39]

Chatgpt for shaping the future of dentistry: the potential of multi- modal large language model,

H. Huang, O. Zheng, D. Wang, J. Yin, Z. Wang, S. Ding, H. Yin, C. Xu, R. Yang, Q. Zhenget al., “Chatgpt for shaping the future of dentistry: the potential of multi- modal large language model,”International Journal of Oral Science, vol. 15, no. 1, p. 29, 2023

work page 2023

[40] [40]

Deep learning for natural language processing: advantages and challenges,

H. Li, “Deep learning for natural language processing: advantages and challenges,”National Science Review, vol. 5, no. 1, pp. 24–26, 2018

work page 2018

[41] [41]

RoBERTa: A Robustly Optimized BERT Pretraining Approach

Y . Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V . Stoyanov, “Roberta: A robustly optimized bert pretraining approach,” arXiv preprint arXiv:1907.11692, 2019

work page internal anchor Pith review Pith/arXiv arXiv 1907

[42] [42]

Bert post-training for review reading comprehension and aspect-based sentiment analysis,

H. Xu, B. Liu, L. Shu, and P. S. Yu, “Bert post-training for review reading comprehension and aspect-based sentiment analysis,” inProceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, 2019, ...

work page 2019

[43] [43]

Optimization techniques for sentiment analysis based on llm (gpt-3),

T. Zhan, C. Shi, Y . Shi, H. Li, and Y . Lin, “Optimization techniques for sentiment analysis based on llm (gpt-3),” arXiv preprint arXiv:2405.09770, 2024

work page arXiv 2024

[44] [44]

Simulating Social Media Using Large Language Models to Evaluate Alternative News Feed Algorithms,

P. T ¨ornberg, D. Valeeva, J. Uitermark, and C. Bail, “Simulating social media using large language models to evaluate alternative news feed algorithms,”arXiv preprint arXiv:2310.05984, 2023

work page arXiv 2023

[45] [45]

What do llms know about financial markets? a case study on reddit market sentiment analysis,

X. Deng, V . Bashlovkina, F. Han, S. Baumgartner, and M. Bendersky, “What do llms know about financial markets? a case study on reddit market sentiment analysis,” inCompanion Proceedings of the ACM Web Conference 2023, 2023, pp. 107–110

work page 2023

[46] [46]

Sentiment analysis algorithms and applications: A survey,

W. Medhat, A. Hassan, and H. Korashy, “Sentiment analysis algorithms and applications: A survey,”Ain Shams engineering journal, vol. 5, no. 4, pp. 1093–1113, 2014

work page 2014

[47] [47]

A survey on sentiment analysis methods, applications, and challenges,

M. Wankhade, A. C. S. Rao, and C. Kulkarni, “A survey on sentiment analysis methods, applications, and challenges,”Artificial Intelligence Review, vol. 55, no. 7, pp. 5731–5780, 2022

work page 2022

[48] [48]

A survey on sentiment analysis challenges,

D. M. E.-D. M. Hussein, “A survey on sentiment analysis challenges,”Journal of King Saud University-Engineering Sciences, vol. 30, no. 4, pp. 330–338, 2018

work page 2018

[49] [49]

Sentiment analysis: a comparative study on different approaches,

M. D. Devika, C. Sunitha, and A. Ganesh, “Sentiment analysis: a comparative study on different approaches,” Procedia Computer Science, vol. 87, pp. 44–49, 2016

work page 2016

[50] [50]

Comparing and combining sentiment analysis methods,

P. Gonc ¸alves, M. Ara ´ujo, F. Benevenuto, and M. Cha, “Comparing and combining sentiment analysis methods,” inProceedings of the first ACM conference on Online social networks, 2013, pp. 27–38

work page 2013

[51] [51]

A survey of sentiment analysis in social media,

L. Yue, W. Chen, X. Li, W. Zuo, and M. Yin, “A survey of sentiment analysis in social media,”Knowledge and Information Systems, vol. 60, pp. 617–663, 2019

work page 2019

[52] [52]

Sen- timent analysis on social media,

F. Neri, C. Aliprandi, F. Capeci, and M. Cuadros, “Sen- timent analysis on social media,” in2012 IEEE/ACM international conference on advances in social networks analysis and mining. IEEE, 2012, pp. 919–926

work page 2012

[53] [53]

Evolutionary dynamics of the cryptocurrency market,

A. ElBahrawy, L. Alessandretti, A. Kandler, R. Pastor- Satorras, and A. Baronchelli, “Evolutionary dynamics of the cryptocurrency market,”Royal Society open science, vol. 4, no. 11, p. 170623, 2017

work page 2017

[54] [54]

Lit- erature review: Machine learning techniques applied to financial market prediction,

B. M. Henrique, V . A. Sobreiro, and H. Kimura, “Lit- erature review: Machine learning techniques applied to financial market prediction,”Expert Systems with Applications, vol. 124, pp. 226–251, 2019

work page 2019

[55] [55]

Sentiment-induced bubbles in the cryptocurrency market,

C. Y .-H. Chen and C. M. Hafner, “Sentiment-induced bubbles in the cryptocurrency market,”Journal of Risk and Financial Management, vol. 12, no. 2, p. 53, 2019

work page 2019

[56] [56]

The state- of-the-art in twitter sentiment analysis: A review and benchmark evaluation,

D. Zimbra, A. Abbasi, D. Zeng, and H. Chen, “The state- of-the-art in twitter sentiment analysis: A review and benchmark evaluation,”ACM Transactions on Manage- ment Information Systems (TMIS), vol. 9, no. 2, pp. 1–29, 2018

work page 2018

[57] [57]

Public sentiment analysis and topic modeling regarding covid-19 vaccines on the reddit social media platform: A call to action for strengthening vaccine confidence,

C. A. Melton, O. A. Olusanya, N. Ammar, and A. Shaban- Nejad, “Public sentiment analysis and topic modeling regarding covid-19 vaccines on the reddit social media platform: A call to action for strengthening vaccine confidence,”Journal of Infection and Public Health, vol. 14, no. 10, pp. 1505–1512, 2021

work page 2021

[58] [58]

Twitter sentiment analysis with a deep neural network: An enhanced approach using user behavioral information,

A. S. M. Alharbi and E. de Doncker, “Twitter sentiment analysis with a deep neural network: An enhanced approach using user behavioral information,”Cognitive Systems Research, vol. 54, pp. 50–61, 2019

work page 2019

[59] [59]

Decoding social sentiment in dao: A comparative analysis of blockchain governance communities,

Y . Quan, X. Wu, W. Deng, and L. Zhang, “Decoding social sentiment in dao: A comparative analysis of blockchain governance communities,” in2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS- C). IEEE, 2024, pp. 216–224. [Online]. Available: https://doi.org/10.1109/QRS-C63300.2024.00037

work page doi:10.1109/qrs-c63300.2024.00037 2024

[60] [60]

Forecasting Economics and Financial Time Series: ARIMA vs. LSTM

S. Siami-Namini and A. S. Namin, “Forecasting eco- nomics and financial time series: Arima vs. lstm,”arXiv preprint arXiv:1803.06386, 2018

work page internal anchor Pith review Pith/arXiv arXiv 2018

[61] [61]

Machine learning methods that economists should know about,

S. Athey and G. W. Imbens, “Machine learning methods that economists should know about,”Annual Review of Economics, vol. 11, no. 1, pp. 685–725, 2019

work page 2019

[62] [62]

J. D. Hamilton,Time series analysis. Princeton university press, 2020

work page 2020

[63] [63]

A multimodal event- driven lstm model for stock prediction using online news,

Q. Li, J. Tan, J. Wang, and H. Chen, “A multimodal event- driven lstm model for stock prediction using online news,” IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 10, pp. 3323–3337, 2020. APPENDIXA DETAILEDCONTRIBUTIONS TORELATEDWORK This study advances four interconnected research streams. Metaverse and Decentraland Research.While pri...

work page 2020

[64] [64]

and Decentraland’s specific implementation [ 4, 29], this study is among the first to empirically link platform-specific community sentiment to token price dynamics (see Table III for performance metrics). We demonstrate that DAO-governed virtual worlds [ 28] generate user-generated content [ 33, 34] with measurable but bounded predictive signal for MANA ...

work page

[65] [65]

and Reddit-style market sentiment analysis [ 45] within decentralized virtual communities. Cryptocurrency Prediction and Volatility.Addressing the evolutionary dynamics of crypto markets [ 53], our LSTM- based multi-modal framework responds to calls for advanced prediction methodologies [ 54]. We contribute to time series forecasting literature [ 60, 62] ...

work page