Abstract Bitcoin is an online currency that is used worldwide to make online payments. It has consequently become an investment vehicle in itself and is traded in a way similar to other open currencies. The ability to predict the price fluctuation of Bitcoin would therefore facilitate future investment and payment decisions. In order to predict the price fluctuation of Bitcoin, we analyse the comments posted in the Bitcoin online forum. Unlike most research on Bitcoin-related online forums, which is limited to simple sentiment analysis and does not pay sufficient attention to note-worthy user comments, our approach involved extracting keywords from Bitcoin-related user comments posted on the online forum with the aim of analytically predicting the price and extent of transaction fluctuation of the currency. The effectiveness of the proposed method is validated based on Bitcoin online forum data ranging over a period of 2.8 years from December 2013 to September 2016.

Citation: Kim YB, Lee J, Park N, Choo J, Kim J-H, Kim CH (2017) When Bitcoin encounters information in an online forum: Using text mining to analyse user opinions and predict value fluctuation. PLoS ONE 12(5): e0177630. https://doi.org/10.1371/journal.pone.0177630 Editor: Kim-Kwang Raymond Choo, University of Texas at San Antonio, UNITED STATES Received: January 16, 2017; Accepted: May 1, 2017; Published: May 12, 2017 Copyright: © 2017 Kim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All relevant data are within the paper and its Supporting Information files. Funding: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science, ICT and future Planning(NRF-2016M3C1B6929629, NRF-2016M3C1B6929579, NRF-2017R1A2B2005380) and Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIP)(R7518-16-1028,High performance computing (HPC) based rendering solution development). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.

Introduction The advancement of the ubiquitous Internet has resulted in the emergence of unprecedented types of currencies that are distinct from the established currency system. The rise of these so-called cryptocurrencies, of which the total supply is increased by using a unique method known as “mining”, has changed the way economic transactions are conducted among Internet users to a great extent. Following the introduction of Bitcoin in 2008[1], a range of cryptocurrencies comparable to Bitcoin have come into existence since 2010[2–4]. Currently, Bitcoin and other cryptocurrency variants are often used for online payments and transactions[4–6] with their circulation gradually increasing over time[3, 6]. In parallel with the increasing circulation of Bitcoin, a growing number of Bitcoin users take to social media or online Bitcoin forums to share information[6]. Yet, despite the plethora of information posted by Bitcoin users, the linkage between such postings and Bitcoin transactions has not been well-documented. The present research builds on previous findings regarding Bitcoin-related online forums, and proposes a method to analytically predict the fluctuations in Bitcoin transaction counts and value using the data collected from user comments posted on the online forum. First, we extracted keywords of interest from user comments on the online forum. We analysed the relationship between the Bitcoin transaction count and price based on the extracted keywords and quantification. Then, we developed a model based on deep learning[7, 8] to predict the Bitcoin transaction count and price. The proposed method efficiently processed the readily accessible online data, and identified as well as utilized the elements that online forum users perceived as important.

Related work Research on cryptocurrencies, particularly on Bitcoin, has been extensively conducted from diverse perspectives, e.g. the analysis of user sentiment as manifested by social media including Twitter[9, 10]. The aim is to determine the value of Bitcoin relative to social phenomena and incidents that have taken place since the introduction of the currency. These social phenomena and incidents include research on the extent to which Bitcoin price fluctuations are related to web search query volumes on Google Trend and Wikipedia, i.e. the extent to which these query volumes predict the Bitcoin price and trade volume[11–14]. Some recent research has focused on the characteristics of Bitcoin online forums. People who share common interests tend to post comments concerning certain topics on online forums[15–19]. Bitcoin is mostly traded on the web with many users making buying/selling decisions based on information acquired on the Internet[6, 20]. Therefore, it is possible to observe how users respond to daily Bitcoin price fluctuations, and to identify or predict future fluctuations in the Bitcoin price and trade volume [6, 20]. In addition, forum users are analysed and classified into Bitcoin user groups[6]. Some researchers simply analysed sentiments based on comments posted by forum users or focused on users per se without considering the information derived from cumulative user comment data gathered during a sample period[17, 21, 22], while others analysed online user comments. In this regard, topic modelling has been actively explored as an effective technique for analysing user opinions from their online textual postings[23]. Topic modelling[24, 25] is a text-mining technique that extracts a set of prevailing topics and relevant keywords out of a large-scale document corpus. This topical information provides users with an instant overview of the corpus, thereby obviating the need to read through comments, which would otherwise be a tedious, time-consuming process. Recently, collaborative filtering and topic modelling have been integrated for generating scientific article recommendation systems on an online community[26]. A Temporal Latent Dirichlet Allocation (TM-LDA) system was used to conduct an in-depth analysis of the online social community by employing an advanced Latent Dirichlet Allocation (LDA) topic modelling algorithm[27]. Likewise, application of the LDA approach to Chinese social reviews revealed the sentiments underlying some social events and services[28].

Discussion We analysed the user comments posted on a Bitcoin online forum to predict the fluctuation in the Bitcoin price and transaction count. Based on the easily accessible online data, the proposed method predicted the Bitcoin price fluctuation with an accuracy rate of over 80%. Moreover, online user postings influenced Bitcoin transactions. The proposed method shed light on some aspects of Bitcoin-related user comments affecting their decisions to buy/sell the cryptocurrency. The causality test result indicated some topics associated with Bitcoin transactions. The Granger causality test result highlighted the concept ‘China’ as having a high causality toward the Bitcoin price with the p-value being 0.05 or less, which was significant. These findings suggest China exerts a strong influence on the Bitcoin price. Furthermore, such concepts as Blockchain’, ‘Altcoin’, and ‘Transaction’ had a high causality toward Bitcoin transaction count with the p-value being 0.05 or less, which was significant. This finding suggests that topics related to the circulation and transaction of other types of cryptocurrencies have an impact on the Bitcoin transaction volume. In addition, the correlation test found significant linear relations in most concepts, excluding ‘Silkroad’, which showed an insignificant linear relation. Hence, the experimental findings revealed some user comments that had the most significant relationship with and effects on the fluctuation in Bitcoin price and transactions. That said, the proposed method has a limitation in terms of its broader applicability due to the fact that the concepts were constructed for a long period of time. For instance, the correlation coefficient of the concept ‘Silkroad’ was 0 or lower even though its construction was based on topics often mentioned by users in relation to some events taking place during a certain period, which hindered the extension of the analysis of the concept to the entire sample period. Thus, appropriate subdivision of the sample period would help to obtain a more accurate understanding of the users for topic modelling and to refine the analysis with additional approaches including sentiment analysis. Moreover, the present findings warrant further studies on the analysis of user comments relative to the characteristics of Bitcoin forums. To increase the accuracy of prediction, it is necessary to address a few challenges. The present work is focused on analysing online forum user comments and adds some formal or structured data to predict the fluctuation in the Bitcoin price and transactions. However, it may add to the reliability of the findings if the search results and relevant content on search engines were quantitatively analysed or if the social network data were analysed as they did in some comparable previous studies[21, 40]. Furthermore, it may be an efficient preliminary study to analyse and classify online forum users per se[41–45]. In addition, the postings may be worth filtering more meticulously [46–50] to more accurately corroborate the findings. Information derived from online forum users seems to be well-suited for extensive research on cryptocurrencies as well as Bitcoin. In the same vein, keywords manifested in online forum user comments could be used for further in-depth analysis and understanding of cryptocurrency transactions. Online forum users’ propensities could also be a cue to identify the characteristics inherent in each cryptocurrency. Moreover, online forums are great sources of abundant informal and formal information, which serves to appreciate cryptocurrencies from diverse perspectives including money laundering, which is closely associated with cryptocurrencies [51–54].

Conclusion With the increasing circulation of Bitcoin, its acceptability has drawn much attention in many ways [2, 3, 5, 14]. The present study is noteworthy in that it analysed the topics often mentioned by Bitcoin users and linked their meanings to Bitcoin transactions. The proposed method for predicting the fluctuation in the Bitcoin price and transactions based on user opinions on online forums is conducive to understanding a range of cryptocurrencies other than Bitcoin and increasing their usability, although it needs to be reinforced. In addition, the present approach to the salience of user comments on online forums is likely to yield more significant results in many other fields.

Author Contributions Conceptualization: YBK CHK. Data curation: JL JC NP. Formal analysis: YBK JL JC CHK. Investigation: YBK JL JC. Methodology: YBK JL JC CHK. Project administration: YBK JC CHK. Software: JL NP JC. Supervision: JC CHK. Validation: YBK NP. Visualization: YBK JL JHK. Writing – original draft: YBK JL NP JC CHK. Writing – review & editing: YBK JL JC JHK.