Blockchain Papers

Meltem Kavaklı, Kadriye Filiz Balbal

This study proposes a hybrid forecasting framework that integrates sentiment analysis with deep learning to predict Bitcoin’s hourly and daily closing prices. Hourly BTC/USD market data spanning June 2021 to November 2025 were combined with approximately 326,000 Bitcoin-related news headlines published over the same period. Sentiment scores in the range of [-1, +1] were generated for each headline using FinBERT, a transformer-based language model trained on financial texts, and were subsequently integrated with technical indicators such as trading volume, MACD, and RSI. The resulting combined feature set was modeled using an LSTM network to capture temporal dependencies. Empirical results demonstrate that sentiment-enhanced hybrid models consistently outperform models based solely on technical indicators across RMSE, MAE, MAPE, and R² metrics. The hourly hybrid model achieved the best performance, with an RMSE of 1,009 USD and an R² of 99.23%. Furthermore, a 30-day out-of-sample real-time evaluation yielded an RMSE of 941 USD. The consistency between in-sample and out-of-sample results indicates that the proposed framework maintains stable predictive performance over time.

Alexandra Conda, Ștefan Găman, Raul Cristian Bag, Miruna Mazurencu-Marinescu-Pele · 6 authors

Abstract This study investigates the relationship between Facebook sentiment and Bitcoin market dynamics using AI-based emotion detection. We analyze 120,000 Facebook posts collected via CrowdTangle alongside Bitcoin financial data from the Blockchain Research Center, covering 2015–2023. Employing FinBERT for sentiment classification, we develop novel compound sentiment scores that integrate text-based sentiment with Facebook’s multi-reaction engagement system, then apply four analytical components: sentiment analysis, Dynamic Topic Modeling, sentiment-based trading strategies, and machine learning volume prediction. Results demonstrate that Facebook sentiment has substantial predictive power for Bitcoin trading volume. Sentiment-based trading strategies significantly outperform buy-and-hold, achieving superior cumulative returns and risk-adjusted performance. For volume prediction, Linear Regression and Bidirectional LSTM achieve comparable test performance, indicating that model complexity does not guarantee superior prediction. Topic modeling reveals that cryptocurrency investment and trading discussions dominate Bitcoin discourse on Facebook, with themes evolving over time in response to market conditions. This research contributes by being the first to apply post-level NLP sentiment analysis of Facebook data to cryptocurrency markets, extending beyond the Twitter and Reddit focus of prior research. The findings provide practical tools for traders and analysts navigating volatile digital asset markets while demonstrating that Facebook’s demographically diverse user base and rich reaction system offer unique advantages for sentiment quantification.

S Pavithra, S. Chitrakala

Social media platforms such as X (formerly Twitter) increasingly shape attention formation, market visibility, and value signaling in electronic commerce, particularly in emerging digital asset markets such as Non-Fungible Tokens (NFTs). Prior work shows that social engagement correlates with NFT prices, suggesting its potential for valuation support. However, open social platforms exhibit heterogeneous user credibility, automated activity, and coordinated promotion, which can distort engagement-based inference. To address these challenges, we propose NFT-TRUST, a trust-aware social signal modeling framework that transforms raw engagement into credibility- and integrity-aware indicators for robust valuation support under manipulation-prone conditions. The framework integrates three components: (i) Credibility-Weighted Social Signal Aggregation (CW-SSA), (ii) Engagement Disproportionality Detection (EDD), and (iii) Integrity-Aware Signal Attenuation (IASA), which jointly reduce the influence of unreliable or manipulated signals while preserving informative engagement. Rather than estimating intrinsic NFT value from social signals alone, NFT-TRUST evaluates the reliability of social attention and converts it into trust-aware features. An XGBoost-based model is used to capture non-linear interactions among these features. Robustness is assessed through stress testing with RL-TweetGen-ST, a reinforcement learning–based synthetic tweet generator that simulates controlled engagement inflation. Experimental results show that NFT-TRUST achieves competitive predictive performance while demonstrating improved stability under simulated manipulation. Ablation analysis indicates that credibility and integrity components are complementary and jointly enhance the reliability of social-signal-based inference. Overall, this work advances trust-aware analytics in electronic commerce and supports more reliable social-driven valuation in emerging digital markets.

Petr Hajek, Josef Novotny, Michal Munk, Daša Munková

The rapid expansion of cryptocurrency markets has coincided with the growing prominence of social media platforms as influential channels for shaping investor sentiment. Among these platforms, YouTube has become a medium for disseminating investment opinions and behavioral signals. This study investigates the extent to which YouTube-derived features—such as video influence scores, sentiment embedded in video titles, and user engagement indicators—can enhance the prediction of Bitcoin price fluctuations. A novel dataset is compiled, covering the period from January 2015 to September 2025. Sentiment is assessed using a combination of transformer-based language models, while influence metrics are computed through engagement statistics adjusted for temporal decay and relevance. These features are combined with historical Bitcoin price data and applied within an XGBoost forecasting framework. The empirical findings suggest that augmenting price-based models with YouTube-related sentiment and engagement features yields a notable improvement in directional forecasting accuracy, outperforming price-only benchmarks by approximately 4 %. Moreover, the study highlights that YouTube-derived behavioral signals offer predictive insights that are not fully captured by conventional indicators such as Google Trends or the Crypto Fear and Greed Index.

sarah Osama anis, Mohammed Mabrouk Morsey, Mostafa Aref

In the rapidly evolving landscape of cryptocurrency, gaining a deep understanding of public sentiment has become increasingly essential, especially given the significant impact of social media platforms on market perceptions and trends. This paper introduces a sophisticated sentiment classification model that utilizes a Bi-LSTM architecture to analyse over one million tweets related to Bitcoin. By integrating Explainable AI techniques, particularly LIME (Local Interpretable Model-agnostic Explanations) framework, our model not only achieves an impressive test accuracy of 98% but also offers valuable insights into its decision-making process, making the results more interpretable for users Our findings highlight robust performance metrics across precision, recall, and F1-scores, which collectively underscore the model's reliability and effectiveness in real-world applications. Furthermore, we delve into the opaque nature of the Bi-LSTM model through the application of LIME, which sheds light on how particular words and phrases have a strong impact on sentiment predictions. This research equips future investigations with conceptual frameworks and analytical tools that can be customized to study a broader range of cryptocurrencies. Through this work, we aim to foster a more nuanced comprehension of how public sentiment shapes market behaviour and decision-making in the digital currency space.

Owen Dugan, Garcia, Roberto, Ronny Junkins, Jerry Liu · 8 authors

The success of large language models (LLMs) can be attributed in part to their ability to efficiently store factual knowledge as key-value mappings within their MLP parameters. Recent work has proposed explicit weight constructions to build such fact-storing MLPs, providing an improved understanding of LLM fact storage mechanisms. In this paper, we introduce an MLP construction framework that improves over previous constructions in three areas: it 1) works for all but a measure-zero set of feasible input-output pairs, 2) achieves asymptotically optimal parameter efficiency matching information-theoretic bounds for some embeddings, and 3) maintains usability within Transformers for factual recall. Through our improvements, we 1) discover a metric on value embeddings that characterizes facts-per-parameter scaling for both constructed and gradient-descent-trained MLPs, 2) identify a simple encoder-decoder mechanism that empirically matches gradient-descent MLP facts-per-parameter asymptotics across all the inputs and outputs we test, and 3) uncover a fundamental tradeoff between an MLP's fact-storage capacity and its usability within Transformers. Finally, we demonstrate a proof-of-concept application of fact-storing MLPs: modular fact editing on one-layer Transformers by \textit{replacing entire MLPs at once}.

Tulika Shrivastava, Basem Suleiman, Sachit A. J. Desa, Muhammad Johan Alibasa · 6 authors

Abstract The volatility of cryptocurrencies necessitates reliable short-term price prediction models for informed investment decisions. This work presents two benchmarking studies that predict cryptocurrency price over hourly and daily time horizons using market indicators and social media data. Study 1 used BERT-based sentiment analysis of hourly Twitter data combined with financial indicators, while Study 2 applied VADER sentiment analysis to daily Twitter and Google Trends data alongside financial indicators. Both studies systematically evaluated statistical models (ARIMA, ARIMAX), machine learning approaches (SVR), and deep learning architectures (1D-CNN, LSTM) including ensemble, multi-modal, and hybrid configurations. Particular attention was given to the influence of lag periods, data aggregation, and sentiment analysis nuances on cryptocurrency price. Empirical results identify LSTM as the best-performing singular prediction model, achieving a 64.5% reduction in RMSE (4.56e $$-$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>-</mml:mo> </mml:math> 03) compared with the SVR baseline in Study 1. In Study 2, the hybrid LSTM + ARIMA model delivered the strongest performance, reducing RMSE by 32.5% (RMSE=2.55e+02) relative to the best performing singular baseline. Hybrid architectures combining LSTM with ARIMA or ARIMAX consistently achieved the lowest RMSE values, outperforming all other configurations and proving especially effective at capturing price movements and turning points. These findings demonstrate how combining statistical methods with deep learning can address non-stationarity, improve sentiment preprocessing, and enhance model interpretability.

S. Chitrakala, Pavithra S S

In the rapidly evolving landscape of digital marketing and electronic commerce, short-form content—particularly on platforms like Twitter (now X)—has become pivotal for real-time branding, community engagement, and product promotion. The rise of Non-Fungible Tokens (NFTs) and Web3 ecosystems further underscores the need for domain-specific, engagement-oriented social media content. However, automating the generation of such content while balancing linguistic quality, semantic relevance, and audience engagement remains a substantial challenge. To address this, we propose RL-TweetGen, a socio-technical framework that integrates instruction-tuned large language models (LLMs) with reinforcement learning (RL) to generate concise, impactful, and engagement-optimized tweets. The framework incorporates a structured pipeline comprising domain-specific data curation, semantic classification, and intent-aware prompt engineering, and leverages Parameter-Efficient Fine-Tuning (PEFT) with LoRA for scalable model adaptation. We fine-tuned and evaluated three LLMs—LLaMA-3.1-8B, Mistral-7B Instruct, and DeepSeek 7B Chat—guided by a hybrid reward function that blends XGBoost-predicted engagement scores with expert-in-the-loop feedback. To enhance lexical diversity and contextual alignment, we implemented advanced decoding strategies, including Tailored Beam Search, Enhanced Top-p Sampling, and Contextual Temperature Scaling. A case study focused on NFT-related tweet generation demonstrated the practical effectiveness of RL-TweetGen. Experimental results showed that Mistral-7B achieved the highest lexical fluency (BLEU: 0.2285), LLaMA-3.1 exhibited superior semantic precision (BERT-F1: 0.8155), while DeepSeek 7B provided balanced performance. Overall, RL-TweetGen presents a scalable and adaptive solution for marketers, content strategists, and Web3 platforms seeking to automate and optimize social media engagement. The framework advances the role of generative AI in digital commerce by aligning content generation with platform dynamics, user preferences, and marketing goals.

Hae Sun Jung, Haein Lee, Jang Hyun Kim

As Bitcoin continues to establish itself as a global asset and discussions around relevant regulations become more active, there is an increasing demand for a comprehensive price prediction framework. To address this necessity, this study aims to enhance the accuracy of Bitcoin price predictions by integrating sentiment information with technical indicators, on-chain data, and cryptocurrency price data. Recognizing Bitcoin’s sensitivity to market sentiment, the proposed framework incorporates sentiment features derived from both lexicon-based methods and large language models. As unsupervised sentiment tools can introduce label noise particularly in domain-specific or ambiguous financial contexts, this study combines the outputs of multiple sentiment models at the feature level to construct a more stable representation. This design improves the robustness of downstream regression performance and distinguishes the framework from previous hybrid models that relied on a single sentiment source without component-wise evaluation. Experimental results using a dataset spanning 2700 days showed that the long short-term memory (LSTM) model with a 3-day window achieves the best performance with mean absolute percentage error (MAPE) of 3.93% and R-squared value of 0.99106. Feature importance analysis further demonstrates sentiment index as the most impactful feature, as excluding it resulted in the largest decline in predictive accuracy. Additionally, the model's performance was evaluated under four major volatility periods, revealing MAPE values ranging from 1.49 to 4.03%, highlighting the framework’s practical capability in rapidly adapting to sudden market shifts. In summary, integrating sentiment information attained from multiple language models significantly enhanced prediction accuracy compared to single source approaches. These findings highlight the framework’s practical value for sentiment-informed investment strategies and risk alerts, with a modular design that enables flexible adaptation and potential integration into automated trading systems.

Khaled Shaalan, Hana Yousuf, Ali Q Saeed, Abdalla Elnekiti · 7 authors

Sentiment analysis in financial texts plays a vital role in understanding market trends, especially in decentralized finance (DeFi) markets where information flow is rapid and largely unstructured. The emergence of large language models has significantly enhanced the ability to extract meaningful insights from unstructured data sources. However, existing sentiment analysis models often underperform on decentralized market texts due to their reliance on traditional financial datasets and their inability to capture domain-specific language, slang, and context. These models typically struggle with the informal, volatile, and jargon-rich nature of DeFi communications found in forums, social media, and blockchain-related discussions. To address these limitations, this paper propose Decentralized Finance Sentiment Extraction using a RoBERTa-based Neural Transformer (DeFiSENT). This framework incorporates a domain-adapted preprocessing pipeline tailored to DeFi language. It fine-tunes the RoBERTa model on curated, labeled datasets from decentralized platforms such as Reddit, Twitter, and DAO channels. Additionally, DeFiSENT integrates context-aware tokenization and class imbalance strategies to enhance performance in detecting sentiment across imbalanced and noisy text data. The proposed method can be deployed in applications such as real-time crypto sentiment dashboards, DeFi asset risk monitoring, and automated trading signal generation, offering a robust tool for both researchers and investors. Experimental evaluations demonstrate that DeFiSENT significantly outperforms baseline models in accuracy, F1-score, and generalization across unseen DeFi datasets. It effectively captures nuanced sentiments and outliers in dynamic financial discourse, providing timely and interpretable sentiment insights within decentralized finance ecosystems.

Minh Doan

This study explores the intricate relationship between public sentiment and Bitcoin market dynamics, leveraging sentiment analysis of Twitter data to uncover patterns in emotional discourse surrounding cryptocurrency. By analyzing sentiment trends from 2013 to 2019, the research reveals a cyclical interplay between positive and negative sentiment, often aligning with Bitcoin’s dramatic price movements. Positive sentiment peaks coincide with periods of market optimism, driven by narratives of technological innovation and mainstream adoption, while negative sentiment troughs reflect moments of fear, uncertainty, and doubt (FUD) during market corrections. Despite the observed alignment, the correlation between sentiment and Bitcoin prices remains weak, underscoring the complexity of market behavior and the influence of external factors such as macroeconomic trends and regulatory developments. The findings highlight the potential of sentiment analysis as a complementary tool for market prediction, offering valuable insights into the emotional undercurrents that shape cryptocurrency markets. This study contributes to a deeper understanding of the socio-economic and psychological dimensions of Bitcoin, providing a foundation for future research in sentiment-driven market analysis.

Kolja de Cuveland, Elisa Choi, M. Minsuk Shin

This exploratory study introduces a sentiment-based framework for the social dynamics of hype around emerging technologies to support strategic investment decisions and contribute to innovation and strategic management research. Drawing on theories of herding behaviour and information cascades, we analysed social media sentiment of the Reddit discourse and investigated bubbles in representative financial assets across four emerging technology cases ‘Metaverse’, ‘Decentralized Finance (DeFi)’, ‘Non-fungible Token (NFT)’, and ‘Hydrogen Economy’ over one-year periods. Additionally, we compared results against measures of search volumes, news coverage, patent filings, and academic publications. We find two essential characteristics of the social dynamics of hype: intensifying positive sentiment and increasing conformity of sentiment towards a positive majority. Since hype can distort decision-making and hinder objective innovation assessments, this study offers practitioners a sentiment-based approach to navigate speculative hype and support decision-making.

Dimple Tiwari, Bhoopesh Singh Bhati, Bharti Nagpal, Amal Al‐Rasheed · 6 authors

Social media has attracted society for decades due to its reciprocal and real-life nature. It influenced almost all societal entities, including governments, academics, industries, health, and finance. The Social Network generates unstructured information about brands, political issues, cryptocurrencies, and global pandemics. The major challenge is translating this information into reliable consumer opinion as it contains jargon, abbreviations, and reference links with previous content. Several ensemble models have been introduced to mine the enormous noisy range on social platforms. Still, these need more predictability and are the less-generalized models for social sentiment analysis. Hence, an optimized stacked-Long Short-Term Memory (LSTM)-based sentiment analysis model is proposed for cryptocurrency price prediction. The model can find the relationships of latent contextual semantic and co-occurrence statistical features between phrases in a sentence. Additionally, the proposed model comprises multiple LSTM layers, and each layer is optimized with Particle Swarm Optimization (PSO) technique to learn based on the best hyperparameters. The model's efficiency is measured in terms of confusion matrix, weighted f1-Score, weighted Precision, weighted Recall, training accuracy, and testing accuracy. Moreover, comparative results reveal that an optimized stacked LSTM outperformed. The objective of the proposed model is to introduce a benchmark sentiment analysis model for predicting cryptocurrency prices, which will be helpful for other societal sentiment predictions. A pretty significant thing for this presented model is that it can process multilingual and cross-platform social media data. This could be achieved by combining LSTMs with multilingual embeddings, fine-tuning, and effective preprocessing for providing accurate and robust sentiment analysis across diverse languages, platforms, and communication styles.

Hla Soe Htay, Mani Ghahremani, Stavros Shiaeles

Bitcoin, the pioneering cryptocurrency, is renowned for its extreme volatility and speculative nature, making accurate price prediction a persistent challenge for investors. While recent studies have employed multivariate models to integrate historical price data with social media sentiment analysis, this study focuses on improving an existing univariate approach By incorporating sentiment and tweet volume data into a multivariate framework, we systematically evaluated the benefits of this integration. Among the five LSTM-based models developed for this study, the Multi-LSTM-Sentiment model achieved the best performance, with the lowest mean absolute error (MAE) of 0.00196 and root-mean-square error (RMSE) of 0.00304. These results underscore the significance of including social media sentiment in predictive modelling and demonstrate its potential to enhance decision-making in the highly dynamic cryptocurrency market.

Seung‐Ho Baek, Minwoo Song, Moonsoo Kang

No abstract is available for this record.

Muhammad Falaah, Andry Alamsyah

Web3 technologies—comprising decentralized finance (DeFi), non-fungible tokens (NFTs), and decentralized autonomous organizations (DAOs)—spark significant public discourse, balancing enthusiasm for innovation with skepticism about associated challenges. While previous studies focus on specific Web3 domains, they often overlook the interplay between thematic discussions and emotional traits, limiting insights into adoption dynamics. This study bridges that gap using a two-stage methodology: Stage 1 categorizes Web3 discussions into industries (e.g., Core Infrastructure, DeFi, NFTs, DAOs), and Stage 2 analyzes emotional traits namely optimism, skepticism, frustration, curiosity, and concern. Annotated data from X (formerly Twitter), YouTube, and Reddit are classified using fine-tuned Bidirectional Encoder Representations from Transformers (BERT) models, achieving macro F1-scores of 0.82 for thematic and 0.84 for emotional classification. Findings reveal that curiosity and optimism dominate Core Infrastructure and Decentralized applications (DApps) discussions, while frustration is more prevalent in NFTs and DAOs. These insights provide actionable strategies for addressing barriers and amplifying drivers of Web3 adoption. Future research should explore broader demographic and temporal trends or employ advanced transformer models like the Robustly Optimized BERT Pretraining Approach (RoBERTa) and XLNet to enhance accuracy and depth.

Yunfei Xing, Zuopeng Zhang, Yuming He, Yueqi Li

Abstract As blockchain technology advances, non-fungible tokens (NFTs) are emerging as unconventional assets in the commercial market. However, it is necessary to establish a comprehensive NFT ecosystem that addresses the prevailing public concerns. This study aimed to bridge this gap by analyzing user-generated content on prominent social media platforms such as Twitter, Weibo, and Reddit. Employing text clustering and topic modeling techniques, such as Latent Dirichlet Allocation, we constructed an analytical framework to delve into the intricacies of the NFT ecosystem. Our investigation revealed seven distinct topics from Twitter and Reddit data and eight topics from Weibo data. Weibo users predominantly engaged in reviews and critiques, whereas Twitter and Reddit users emphasized personal experiences and perceptions. The NFT ecosystem encompasses several crucial elements, including transactions, customers, infrastructure, products, environments, and perceptions. By identifying the prevailing trends and common issues, this study offers valuable guidance for the development of NFT ecosystems.

Andry Alamsyah, Raras Fitriyani Astuti

Purpose This study aims to analyze public discourse on decentralized finance (DeFi) and central bank digital currencies (CBDC) using advanced natural language processing (NLP) techniques to uncover key insights that can guide financial policy and innovation. This research seeks to fill the gap in the existing literature by applying state-of-the-art NLP models like BERT and RoBERTa to understand the evolving online discourse around DeFi and CBDC. Design/methodology/approach This study uses a multilabel classification using BERT and RoBERTa models alongside BERTopic for topic modeling. Data is collected from social media platforms, including Twitter and LinkedIn, as well as relevant documents, to analyze public sentiment and discourse. Model performance is evaluated based on accuracy, precision, recall and F1-scores. Findings RoBERTa outperforms BERT in classification accuracy and precision across all metrics, making it more effective in categorizing public discourse on DeFi and CBDC. BERTopic identifies five key topics frequently discussed, such as financial inclusion, competition and growth in DeFi, with important implications for policymakers. Practical implications The insights derived from this study provide valuable information for financial regulators and policymakers to develop more informed, data-driven strategies for implementing and regulating DeFi and CBDC. Public discourse analysis enables policymakers to understand emerging concerns and trends critical for crafting effective financial policies. Originality/value This study is among the first to use advanced NLP models, including RoBERTa and BERTopic, to analyze public discourse on DeFi and CBDC. It offers novel insights into the potential challenges and opportunities these innovations present. It contributes to the growing body of research on the intersection of digital financial technologies and public sentiment.

Shagufta Henna, Mohamed Amjath

Detecting front-running attacks in Ethereum blockchain transactions is crucial for maintaining security and integrity within decentralized ecosystems. However, existing models struggle to accurately model the complex distributions inherent in tabular data, particularly in the presence of class imbalance and mode collapse. This paper leverages the potentials of Conditional Tabular Generative Adversarial Networks and PacGAN, called a Conditional Packing GAN (cPacGAN), to address these challenges. cPacGAN effectively generates synthetic data that closely mimics the distribution of real transactions, thereby augmenting the dataset and improving the performance of front-running attack detection. PacGAN mitigates mode collapse by incorporating packed samples in the discriminator, improving the diversity of generated samples and improving the stability of the training process. Through experimental evaluations of a real-world Ethereum transactions dataset, cPacGAN demonstrates improved performance across all selected machine learning classifiers, particularly augmenting the effectiveness of Tabular Neural Networks (TabNet).

Rahul Reddy, Pushpinder Singh Pateja, Adarsh Patel, Gopinath Palaniappan · 5 authors

Blockchain technology is significant because it makes data sharing between several parties safe, transparent, and effective. Multi-step transactions that require verification and traceability can benefit from Blockchain technology. However, the Blockchain technology too comes with its own vulnerabilities and often Blockchain networks are attacked by attacks like 51% Attack, Eclipse Attack, Sybil Attack, Time jacking Attack, Selfish Mining Attack, Finney Attack, Race Attack and so on. One among those attacks is the Sybil attacks, which are a big threat to the integrity of Blockchain networks since they assist malicious actors to create several identities, potentially overwhelming the system and defeating the very principle of consensus mechanisms. In this paper, we have captured an approach on designing a multi-layered mechanism for identifying Sybil attacks with the integration of behavioral analysis, Blockchain analysis techniques, Zero-Knowledge Proofs (ZKPs), and a robust security architecture for governance and validator selection. The broad idea is to cancel pseudo-anonymity in the Blockchain systems by detecting behavioral patterns, identifying exchange wallets, and mapping inter-wallet relationships. Integration of these approaches with ZKPs assists in improving identity verification while simultaneously maintaining user anonymity. The proposed architecture for security uses community-based governance and adaptive validator selection processes to strengthen the defense against Sybil attacks. Token concentration analysis traces down the distribution of stakes within the network in order to find potential risks due to centralization. Our findings thus conclude that integration of the security framework along with behavior analysis and ZKPs effectively reduces the proliferation of fraudulent identities in the Blockchain networks.

Jiarui Ye, Reetam Biswas, Nilamadhab Mishra

The emergence of cryptocurrencies has dramatically impacted the financial sector, drawing significant attention and sparking widespread debates across platforms like Facebook and Reddit. These discussions offer crucial insights into public sentiment, which uniquely influences cryptocurrency valuations, setting them apart from traditional financial products. Due to this sensitivity to public opinion, both the academic and financial sectors are closely monitoring these dynamics. This paper utilizes Natural Language Processing (NLP) technologies, specifically the DeBERTa model, to perform an in-depth sentiment analysis of cryptocurrency-related discussions on Reddit. Our methodology classifies sentiments into five levels—very positive, positive, neutral, negative, and very negative—allowing for a precise assessment of public emotions and acceptance of cryptocurrencies. The findings provide vital data for market analysis and underscore the significant influence of public sentiment on the cryptocurrency markets.

Uğur Demirel, Handan Çam

This study investigates public sentiment about popular cryptocurrencies listed on crypto exchanges in Turkey, using comments shared on social media platforms and online forums. The research seeks to enhance the existing body of knowledge by overcoming the shortcomings of sentiment analysis studies focused on Turkish texts. Data collected from social media and online forums were examined with sentiment analysis techniques. A total of 607,592 comments were analyzed, of which 89,986 were classified as negative, 72,655 as positive, and 444,951 as neutral. For binary classification, 89,986 negative and 72,655 positive examples were selected and machine-learning models were trained and tested on 162,641 examples. The study's methodology includes an in-depth examination of sentiment analysis results obtained using machine learning classifiers. The findings show how various cryptocurrencies are perceived on different social media platforms. For instance, BTC (Bitcoin) is generally perceived negatively on Investing.com and Telegram, while ETH (Ethereum) generally displays more negative views. These results help investors understand their perceptions and market expectations towards cryptocurrencies. This study deepens the role of social media sentiment analysis in cryptocurrency markets, contributing to the development of new methods and approaches for future research.

Vivek Pandey, Vikas Tiwari, Roshan Sonavane, Gaurav Jha · 6 authors

In a time characterized by mounting apprehensions regarding data security and confidence in digital transactions, there's a growing need for a reliable and transparent framework to oversee the origin and legitimacy of goods along the supply chain. Concerns such as counterfeiting, deceit, and operational inefficiencies are prevalent across various sectors, causing skepticism and financial setbacks. The primary issue stems from the absence of an immutable and decentralized ledger to uphold the accuracy of product data from production to distribution. Through facilitating stakeholders in monitoring product trajectories from inception to delivery, our goal is to bolster trust and alleviate fraudulent activities. In centralized systems, control and decision-making authority are vested in a singular entity or a small group of entities. This concentration of power enables streamlined direction and oversight over all activities within the system. Conversely, decentralization distributes authority across multiple entities or individuals, empowering them to make decisions autonomously within their spheres of influence. Decentralized systems often promote greater flexibility, adaptability, and resilience as decision-making is dispersed, allowing for localized responses and innovation. In today's digital age, individuals and businesses face an overwhelming deluge of textual data, spanning social media updates, customer feedback, and online commentary. Yet, deciphering this unstructured text presents a formidable hurdle, impeding the timely comprehension of public sentiment, viewpoints, and trends. Consequently, there arises a critical necessity for an effective and automated sentiment analysis solution capable of swiftly and accurately gauging sentiment within text, empowering users to glean insights into public perceptions and make well-informed decisions. Our sentiment analysis application endeavors to address this challenge by offering an automated, scalable, and precise sentiment analysis tool suited for diverse applications, encompassing marketing, customer service, brand management, and market research.

Sapternab Chatterjee, Rupali Sunil Wagh

Bitcoin, launched in 2009 as the pioneer cryptocurrency, has caught the eye of investors worldwide due to its nature and remarkable growth. Recent studies indicate that elements, like announcements, news sentiments, government regulations, and overall market sentiments significantly influence the trajectory of bitcoin price. This work presents a comprehensive analysis to provide deeper insights into the relationship between user sentiments expressed as tweets and bitcoin prices (daily price changes). As a significant contribution, the authors propose multiple approaches to performing the non-trivial task of integrating tweet sentiment with bitcoin price data. The chapter also presents the incorporation of inherent lag in the expression of sentiments and their impact on price change by demonstrating lagged sentiment analysis. The work employs state-of-the-art machine learning and deep learning models for analyzing the data both as classification and regression tasks to uncover hidden patterns.