Cryptocurrency Price Prediction using Time Series and Social Sentiment Data

Pang, Yan; Sundararaj, G. Kharmega; Ren, Jiewen

doi:10.1145/3365109.3368785

Cited by 15 publications

(5 citation statements)

References 8 publications

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“…Naeem et al [15] analyzed the relationship between online investor sentiment, measured through Twitter happiness and the FEARS index, and the returns of six major cryptocurrencies, finding that sentiment is a significant predictor of cryptocurrency returns, particularly at extreme market states. Lamon et al [16] developed a model that uses news and social media data to predict price fluctuations of three cryptocurrencies (bitcoin, litecoin, and ethereum) by training the model on labeled data based on actual future price changes, allowing it to directly predict price movements. Pang et al [17] explored the use of machine learning techniques to predict bitcoin prices by modeling the nonlinear relationship between prices and social sentiment data, and found that sentiment-based models, particularly neural networks, outperform traditional methods in capturing this relationship and predicting prices with higher accuracy.…”

Section: Of 32mentioning

confidence: 99%

Dynamics Between Bitcoin Market Trends and Social Media Activity

Vlahavas,

Vakali

2024

Preprint

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This study examines the relationship between Bitcoin market dynamics and user activity on the r/cryptocurrency subreddit. The purpose of this research is to understand how social media activity correlates with Bitcoin price and trading volume, and to explore the sentiment and topical focus of Reddit discussions. We collected data on Bitcoin’s closing price and trading volume from January 2021 to December 2022, alongside the most popular posts and comments from the subreddit during the same period. Our analysis revealed significant correlations between Bitcoin market metrics and Reddit activity, with user discussions often reacting to market changes. Additionally, user activity on Reddit may indirectly influence the market through broader social and economic factors. Sentiment analysis showed that positive comments were more prevalent during price surges, while negative comments increased during downturns. Topic modeling identified four main discussion themes, which varied over time, particularly during market dips. These findings suggest that social media activity on Reddit can provide valuable insights into market trends and investor sentiment. Overall, our study highlights the influential role of online communities in shaping cryptocurrency market dynamics, offering potential tools for market prediction and regulation.

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Section: Of 32mentioning

confidence: 99%

Dynamics Between Bitcoin Market Trends and Social Media Activity

Vlahavas,

Vakali

2024

Preprint

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“…To test our questions, we aim to quantify and visualize users' stance on the community by using sentiment analysis. Applying sentimental analysis based on social media, such as Twitter, Reddit, and Chain node, has been practiced for fiat cryptocurrencies epitomized by studies targeting price prediction of bitcoin and Ethereum in several studies [2,6,16]. As relatively few foundational studies have begun to examine the value of NFTs based on user sentiment, and considering that NFTs can be viewed as an asset class, we expect that our research on the discourse occurring within NFT project communities will yield valuable insights [17].…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the Role of User-Driven Communities in NFT Valuation: A Case Study of Discord

Kim¹,

Lee²,

Ki³

et al. 2022

J Multimed Inf Syst

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There has been a recent increase in interest in NFTs, as the number of high-priced trades has risen. While some research attempts to identify the factors behind these value spikes using traditional market analysis and machine learning, few studies focus on the user-driven communities where vast amounts of information are shared. We aim to address the need for understanding the factors that influence NFT prices within user-driven communities, specifically within Discord, an influential platform for NFT projects. To do this, three NFT projects are selected -Coolman's Universe, Degen Toonz, and Mfer -based on their size, value, and active user base. Chat data are collected from these projects, and three methods are used to gain insights into how communication among users evolves around influential Discourse leaders. The analysis shows that there are distinct ways and styles in which discourse leaders communicate with community members. Furthermore, the common sentimental experience of the leaders regarding the project value is identified to highlight the important aspects of communities. Conducting a social network analysis to examine the leader's significance within the community enables us to take the initial step towards comprehending the value of an NFT from the perspective of a user-driven community.

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“…As discussed in the previous section, several studies [7][8][9][10][11][12][13][14][15][16] have been conducted on the price prediction of cryptocurrencies like Bitcoin and Ethereum. In [5], a feature vector was constructed by taking ten values, five each from market data and twitter sentiment data.…”

Section: Related Workmentioning

confidence: 99%

“…Hence, the performance measurements in this paper focus on real-value prediction evaluation methods. The most commonly used method to measure error in time series models is root-mean squared error (RMSE) [11]. The root-mean-square error (RMSE) is a frequently used measure of the differences between values (sample values and predicted values of a model.…”

Section: Evaluation Measurementioning

confidence: 99%

Prediction of dogecoin price using deep learning and social media trends

Agarwal¹,

Harjule²,

Chouhan³

et al. 2021

EAI Endorsed Transactions on Industrial Networks and Intelligen

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INTRODUCTION: Cryptocurrency is a digital, decentralized form of money based on blockchain technology, which makes it the most secure method of making a transaction. There has been a huge increase in the number of cryptocurrencies in the past few years. Cryptocurrencies such as Bitcoin and Ethereum have become an interesting subject of study in fields such as finance. In 2021, over 4,000 cryptocurrencies are already listed. There are many past studies that focus on predicting the price of cryptocurrencies using machine learning, but the majority of them only focused on Bitcoin. Moreover, the majority of the models implemented for price prediction only used the historical market prices, and do not utilize social signals related to the cryptocurrency. OBJECTIVES: In this paper, we propose a deep learning model for predicting the prices of dogecoin cryptocurrency. The proposed model is based on historical market price data as well as social trends of Dogecoin cryptocurrency. METHODS:The market data of Dogecoin is collected from Kaggle on the granularity of a day and for the same duration the verified tweets have also been collected with hashtags "Dogecoin" and "Doge". Experimental results show that the proposed model yields a promising prediction of future price of Dogecoin, a cryptocurrency that has recently become the talk of the town of the crypto market. RESULTS: Minimum achieved RMSE in predicted price of Dogecoin was 0.02 where the feature vector consisted of OCVP (Open, Close, Volume, Polarity) values from combined dataset. RESULTS: Experimental results show that the proposed approach performs efficiently. s a major factor in the future price of Dogecoin.

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Cryptocurrency Price Prediction using Time Series and Social Sentiment Data

Cited by 15 publications

References 8 publications

Dynamics Between Bitcoin Market Trends and Social Media Activity

Dynamics Between Bitcoin Market Trends and Social Media Activity

Exploring the Role of User-Driven Communities in NFT Valuation: A Case Study of Discord

Prediction of dogecoin price using deep learning and social media trends

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