ArticlePloS one2016
Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Conducting Causal Analysis by Means of Approximating Probabilistic Truths.Entropy (Basel, Switzerland) · 2022Article
- Forecasting and trading cryptocurrencies with machine learning under changing market conditions.Financial innovation · 2021Article
- Bitcoin in the economics and finance literature: a survey.SN business & economics · 2021Article
- A model of the optimal selection of crypto assets.Royal Society open science · 2020Article
- Forecasting Bitcoin Trends Using Algorithmic Learning Systems.Entropy (Basel, Switzerland) · 2020Article
- Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach.Neural computing & applications · 2020Review
- A percolation model for the emergence of the Bitcoin Lightning Network.Scientific reports · 2020Article
- Social media and bitcoin metrics: which words matter.Royal Society open science · 2019Article
- Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning.Entropy (Basel, Switzerland) · 2019Article
- Predicting altcoin returns using social media.PloS one · 2018Article
- Article
- When Bitcoin encounters information in an online forum: Using text mining to analyse user opinions and predict value fluctuation.PloS one · 2017Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper proposes a method to predict fluctuations in the prices of cryptocurrencies, which are increasingly used for online transactions worldwide. Little research has been conducted on predicting fluctuations in the price and number of transactions of a variety of cryptocurrencies. Moreover, the few methods proposed to predict fluctuation in currency prices are inefficient because they fail to take into account the differences in attributes between real currencies and cryptocurrencies. This paper analyzes user comments in online cryptocurrency communities to predict fluctuations in the prices of cryptocurrencies and the number of transactions. By focusing on three cryptocurrencies, each with a large market size and user base, this paper attempts to predict such fluctuations by using a simple and efficient method.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.