Short-Term Bitcoin Price Fluctuation Prediction Using Social Media and Web Search Data

Mittal, Aditi; Dhiman, Vipasha; Singh, Ashi; Prakash, Chandra

doi:10.1109/ic3.2019.8844899

Cited by 72 publications

(70 citation statements)

References 6 publications

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“…In a similar vein, Matta, Lunesu, and Marchesi (2015) found that the volume of tweets and google trend data correlates with bitcoin prices. Kim et al (2016) analyzed online datasets (e.g., postings, replies, views, sentiments) drawn from cryptocurrency communities to predict price fluctuations.…”

Section: Cryptocurrency Study Using Social Media Datamentioning

confidence: 74%

How Are Twitter Activities Related to Top Cryptocurrencies' Performance? Evidence from Social Media Network and Sentiment Analysis

Park

Lee

2019

Drustvena istrazivanja

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Cryptocurrencies have embraced Twitter as a major channel of communication. Employing social network analysis and sentiment analysis, this study investigates the Twitter-mediated communication behaviors among cryptocurrencies. This study determines whether a significant association exists between cryptocurrencies' Twitter networks and their credit scores. Data were drawn from the Twitter pages of several top cryptocurrencies. The results indicate that reply-mention networks had the densest structure, that the following-follower network structure was correlated with the reply-mention structure, and that the reply-mention and co-tweet networks were positively correlated. The results also indicate that cryptocurrencies' active networking strategies affected their credit scores and more importantly, that cryptocurrencies frequently linked with fellow currencies tended to have high credit scores.

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Section: Cryptocurrency Study Using Social Media Datamentioning

confidence: 74%

How Are Twitter Activities Related to Top Cryptocurrencies' Performance? Evidence from Social Media Network and Sentiment Analysis

Park

Lee

2019

Drustvena istrazivanja

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“…The study concluded that exchanges on the outside of the realm of the Blockchain have technically dictated price and it limited the Blockchain data predictability. Similarly, Matta et al [12] studied the effect of tweets on Twitter and Trend views of Google for the price of Bitcoin with 60 days as sample size and sentiment as a variable. The author found that both Google Trend views and positive tweets have moderately correlated to the Bitcoin price fluctuation and that correlation can be used to predict the cryptocurrencies price.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Cryptocurrency Price Prediction by Exploiting IoT Concept and Beyond: Cloud Computing, Data Parallelism and Deep Learning

Premarathne¹,

Halgamuge²,

Samarakody³

et al. 2020

IJACSA

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Cryptocurrency has as of late pulled in extensive consideration in the fields of economics, cryptography, and computer science due to it is an encrypted digital currency, peerto-peer virtual forex produced using codes, and it is much the same as another medium of the trade like real cash. This study mainly focuses to combine the Deep Learning with Data parallelism and Cloud Computing Machine learning engine as "hybrid architecture" to predict new Cryptocurrency prices by using historical Cryptocurrency data. The study has exploited 266,776 of Cryptocurrency prices values from the pilot experiment, and Deep Learning algorithm used for the price prediction. The four hybrid architecture models, namely, (i) standalone PC, (ii) Cloud computing without data parallelism (GPU-1), (iii) Cloud computing with data parallelism (GPU-4), and (iv) Cloud computing with data parallelism (GPU-8) introduced and utilized for the analysis. The performance of each model is evaluated using different performance evaluation parameters. Then, the efficiency of each model was compared using different batch sizes. An experimental result reveals that Cloud computing technology exposes new era by performing parallel computing in IoT to reduce computation time up to 90% of the Deep Learning algorithm-based Cryptocurrencies price prediction model and many other IoT applications such as character recognition, biomedical field, industrial automation, and natural disaster prediction.

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“…Sentiment Analysis has been used extensively to understand the impact of public opinions on the price fluctuations of cryptocurrencies. References [10], [13], [14] found evidence of a strong correlation between the magnitude of public interest (using Google and Wikipedia search volumes as an analog) and the price of Bitcoin. Further, [13], [15]- [17] provided evidence that the polarity of public opinion is powerful for predicting public interest in Bitcoin.…”

Section: Related Workmentioning

confidence: 99%

C2P2: A Collective Cryptocurrency Up/Down Price Prediction Engine

Bai

White

Xiao

et al. 2019

2019 IEEE International Conference on Blockchain (Blockchain)

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We study the problem of predicting whether the price of the 21 most popular cryptocurrencies (according to coinmarketcap.com) will go up or down on day d, using data up to day d−1. Our C2P2 algorithm is the first algorithm to consider the fact that the price of a cryptocurrency c might depend not only on historical prices, sentiments, global stock indices, but also on the prices and predicted prices of other cryptocurrencies. C2P2 therefore does not predict cryptocurrency prices one coin at a time -rather it uses similarity metrics in conjunction with collective classification to compare multiple cryptocurrency features to jointly predict the cryptocurrency prices for all 21 coins considered. We show that our C2P2 algorithm beats out a recent competing 2017 paper by margins varying from 5.1-83% and another Bitcoin-specific prediction paper from 2018 by 16%. In both cases, C2P2 is the winner on all cryptocurrencies considered. Moreover, we experimentally show that the use of similarity metrics within our C2P2 algorithm leads to a direct improvement for 20 out of 21 cryptocurrencies ranging from 0.4% to 17.8%. Without the similarity component, C2P2 still beats competitors on 20 out of 21 cryptocurrencies considered. We show that all these results are statistically significant via a Student's t-test with p < 10 −5 .

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Short-Term Bitcoin Price Fluctuation Prediction Using Social Media and Web Search Data

Cited by 72 publications

References 6 publications

How Are Twitter Activities Related to Top Cryptocurrencies' Performance? Evidence from Social Media Network and Sentiment Analysis

How Are Twitter Activities Related to Top Cryptocurrencies' Performance? Evidence from Social Media Network and Sentiment Analysis

Real-Time Cryptocurrency Price Prediction by Exploiting IoT Concept and Beyond: Cloud Computing, Data Parallelism and Deep Learning

C2P2: A Collective Cryptocurrency Up/Down Price Prediction Engine

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