Empirical Analysis Тowards the Effect of Social Media on Cryptocurrency Price and Volume (original) (raw)
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2021
Along with the fast-pace integration of social media into our everyday life, the same goes to the investment world. In such volatile and manipulative markets like cryptocurrencies', investor sentiment are easily influenced by Key Opinion Leaders (KOLs), fake news, and herding behaviors. We tackled this issue by analyzing the Tweets sentiment using VADER lexicon approach and cross-correlation between lagged tweets sentiment score, tweets volume, and hourly price changes of the four most mentioned altcoins on Twitter (BTC, ETH, XRP, DOGE). We found out that while some cryptocurrencies have been more efficient throughout the time, some are still heavily influenced by social media sentiment. Especially, the value of Dogecoin does not based on its intrinsic value or utility, but fully dependent upon social media sentiment.
Extracting Cryptocurrency Price Movements from the Reddit Network Sentiment
2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), 2019
Explosive growth in the value of cryptocurrencies like Bitcoin and Ethereum in recent years has attracted the attention of many speculators. Unlike traditional currencies, cryptocurrencies are not backed by any government agencies resulting in prices being strongly influenced by public opinion. Understanding the relationship between cryptocurrency prices and the public sentiment can lead to improved predictions of price movement. In this paper, we give an exploratory analysis of a network of 24 Reddit communities related to Bitcoin, Ethereum, or other cryptocurrencies to analyze Bitcoin and Ethereum price movements. We engineer a set of 112 time series features from submissions and comments made on the selected subreddits, run Granger causality tests on engineered time series against cryptocurrency price movements, and use these time series to forecast the cryptocurrency price movements using classification models. Results from these models support the Granger causality test results showing that with only lagged price values and lagged values from a single Reddit data derived feature, the direction of Bitcoin and Ethereum price movements can be predicted with 74.2% and 73.1% accuracy respectively.
Cryptocurrency Price Prediction Using Tweet Volumes and Sentiment Analysis
2018
In this paper, we present a method for predicting changes in Bitcoin and Ethereum prices utilizing Twitter data and Google Trends data. Bitcoin and Ethereum, the two largest cryptocurrencies in terms of market capitalization represent over $160 billion dollars in combined value. However, both Bitcoin and Ethereum have experienced significant price swings on both daily and long term valuations. Twitter is increasingly used as a news source influencing purchase decisions by informing users of the currency and its increasing popularity. As a result, quickly understanding the impact of tweets on price direction can provide a purchasing and selling advantage to a cryptocurrency user or a trader. By analyzing tweets, we found that tweet volume, rather than tweet sentiment (which is invariably overall positive regardless of price direction), is a predictor of price direction. By utilizing a linear model that takes as input tweets and Google Trends data, we were able to accurately predict t...
Investigating an Individual’s Opinion on Social Media About the Cryptocurrency Market
International Scientific Conference „Business and Management“, 2022
Cryptocurrencies are growing rapidly, with various altcoin being introduced recently, despite the fact that the market is very volatile, cryptocurrency now holds trillions of dollars in the market and has plenty of platforms for trading and owning cryptocurrencies, like Binance, Coinbase, and others. In particular, Bitcoin has caught the attention of many people over the year with a current market cap. of 731.56 billion dollars circulating in the market. One of the major problems in cryptocurrencies is volatility, and often the prices can vary due to the external events that trigger the market. That is, Twitter sentiment. The objective of the article is to investigate people's opinion about the cryptocurrency market on social media using collected tweets for 2 popular hashtags of Bitcoin and investigating the tweets using sentiment analysis. The study found that sentiment scores could be related to observed price fluctuations.
Analyzing Cryptocurrency trends using Tweet Sentiment Data and User Meta-Data
arXiv (Cornell University), 2023
Cryptocurrency is a form of digital currency using cryptographic techniques in a decentralized system for secure peer-to-peer transactions. It is gaining much popularity over traditional methods of payments because it facilitates a very fast, easy and secure way of transactions. However, it is very volatile and is influenced by a range of factors, with social media being a major one. Thus, with over four billion active users of social media, we need to understand its influence on the crypto market and how it can lead to fluctuations in the values of these cryptocurrencies. In our work, we analyze the influence of activities on Twitter, in particular the sentiments of the tweets posted regarding cryptocurrencies and how it influences their prices. In addition, we also collect metadata related to tweets and users. We use all these features to also predict the price of cryptocurrency for which we use some regressionbased models and an LSTM-based model.
Impacts of Positive and Negative Comments of Social Media Users to Cryptocurrency
Blockchain implementation brought several benefits to many areas. One of the usages of blockchain is in digital currencies. Digital currency (cryptocurrency) is a new era for the global financial system. Cryptocurrencies draw significant attention from researchers because of their advantages. Although there are several risks (e.g., speculation, 51% attack) related to cryptocurrency, billions of dollars are invested in them, because of their transparency, traceability, low transaction cost, and highly profitable potential. In December 2017, the most famous cryptocurrency, Bitcoin, has reached almost $20,000.00 per coin. Such short-term, high gain potential attracts many new small investors. However, speculative movements raise many questions related to the safety and privacy of investors, just to name a few. To understand public opinions about cryptocurrency and speculative movements to protect small investors financial interests, sentiment analysis can be done by using social media activities of individuals who are interested or investing in cryptocurrencies. It is also one of the essential steps in the analysis to understand the profiles of the users. Therefore, in this paper, we determine the attitudes of social network users by analyzing the positivity and negativity of the comments about six cryptocurrencies. Results show that the positivity is higher than negativity, and there exist relations between price changes and attitudes. However, relations vary according to currency types. The results and analysis, which are provided in this paper, help new investors and developers to obtain opinions of social network users who are interested or investing in cryptocurrency.
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
Since Bitcoin first appeared on the scene in 2009, cryptocurrencies have become a worldwide phenomenon as important decentralized financial assets. Their decentralized nature, however, leads to notable volatility against traditional fiat currencies, making the task of accurately forecasting the crypto-fiat exchange rate complex. This study examines the various independent factors that affect the volatility of the Bitcoin-Dollar exchange rate. To this end, we propose CoMForE, a multimodal AdaBoost-LSTM ensemble model, which not only utilizes historical trading data but also incorporates public sentiments from related tweets, public interest demonstrated by search volumes, and blockchain hash-rate data. Our developed model goes a step further by predicting fluctuations in the overall cryptocurrency value distribution, thus increasing its value for investment decision-making. We have subjected this method to extensive testing via comprehensive experiments, thereby validating the importance of multimodal combination over exclusive reliance on trading data. Further experiments show that our method significantly surpasses existing forecasting tools and methodologies, demonstrating a 19.29% improvement. This result underscores the influence of external independent factors on cryptocurrency volatility. CCS CONCEPTS • Computing methodologies → Ensemble methods; Supervised learning by classification; • Applied computing → Economics; Forecasting; • Human-centered computing → Social media.
System research and information technologies
This article presents an algorithm for predicting the rate of a selected cryptocurrency, taking into account the posts of a group of famous people in a particular social network. The celebrities chosen as experts, i.e., famous personalities whose posts on social networks were studied, are either familiar with the financial industry, particularly the cryptocurrency market, or some cryptocurrency. The dataset used was the actual rates of the cryptocurrency in question for the selected period and the statistics of expert posts in the selected social network. The study used methods such as the full probability formula and the Bayesian formula. It was found that posts by famous people on social media differently affected cryptocurrency rates. The “main” expert was identified, and his posts were used to forecast the selected cryptocurrency’s rate.
Using Time-Series and Sentiment Analysis to Detect the Determinants of Bitcoin Prices
SSRN Electronic Journal, 2000
This paper uses time-series analysis to study the relationship between Bitcoin prices and fundamental economic variables, technological factors and measurements of collective mood derived from Twitter feeds. Sentiment analysis has been performed on a daily basis through the utilization of a state-of-theart machine learning algorithm, namely Support Vector Machines (SVMs). A series of short-run regressions shows that the Twitter sentiment ratio is positively correlated with Bitcoin prices. The short-run analysis also reveals that the number of Wikipedia search queries (showing the degree of public interest in Bitcoins) and the hash rate (measuring the mining difficulty) have a positive effect on the price of Bitcoins. On the contrary, the value of Bitcoins is negatively affected by the exchange rate between the USD and the euro (which represents the general level of prices). A vector errorcorrection model is used to investigate the existence of long-term relationships between cointegrated variables. This kind of long-run analysis reveals that the Bitcoin price is positively associated with the number of Bitcoins in circulation (representing the total stock of money supply) and negatively associated with the Standard and Poor's 500 stock market index (which indicates the general state of the global economy).