Connection between bitcoin and mining

Downpours transform the mottled landscape into lush emerald, while azaleas bloom and migrating cranes and storks begin the long journey back north. The rainfall also brings trucks stacked with computers to hydropower dams, where entrepreneurs can tap cheap electricity for mining bitcoin—the arcane process that accumulates the cryptocurrency using huge amounts of computing power to solve equations. Cryptocurrency mining requires huge amounts of computing power, making energy consumption a major overhead for the industry. Local governments will often offer power for pennies—or even free—to attract jobs and get a painless boost to their gross domestic product figures. While individual miners and traders may be able to slip through the cracks, larger commercial miners will likely be considering alternative mining hubs with less rigorous regulatory regimes, analysts say. Last week, a number of companies involved in cryptocurrency mining began halting operations in China.

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Content:

• Mining Bitcoin with Nuclear Power
• The Political Geography and Environmental Impacts of Cryptocurrency Mining
• Why does Bitcoin need more energy than whole countries?
• Electricity needed to mine bitcoin is more than used by 'entire countries'
• Visualizing the Power Consumption of Bitcoin Mining
• Bitcoin and Potosí Silver: Historical Perspectives on Cryptocurrency
• New Research: Crypto-mining Drives Almost 90% of All Remote Code Execution Attacks
• Crypto Mining: Definition and Function Explained
• Bitcoin miners align with fossil fuel firms, alarming environmentalists

WATCH RELATED VIDEO: Cryptocurrency Mining For Dummies - FULL Explanation

Mining Bitcoin with Nuclear Power

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. 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. 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.

The advancement of the ubiquitous Internet has resulted in the emergence of unprecedented types of currencies that are distinct from the established currency system. Following the introduction of Bitcoin in [ 1 ], a range of cryptocurrencies comparable to Bitcoin have come into existence since [ 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.

Research on cryptocurrencies, particularly on Bitcoin, has been extensively conducted from diverse perspectives, e. 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. 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 ]. 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 ]. Likewise, application of the LDA approach to Chinese social reviews revealed the sentiments underlying some social events and services[ 28 ]. This section provides an overview of the proposed method.

First, we gathered the data relevant to Bitcoin for the purpose of the experiment. More specifically, Bitcoin-related posts on the online forum, daily Bitcoin transaction counts, and its price were gathered. We also extracted and rated significant keywords from the data gathered on the online forum. Then, we selected the data of higher score ratings to generate the prediction model based on deep learning and used the model to predict the fluctuation in the Bitcoin price and transaction count see Fig 1.

Data crawling was the first step in our analysis. We postulated that user comments on the targeted online Bitcoin forum would have an impact on the fluctuation of the Bitcoin price and transaction count. Thus, we crawled and analysed the relevant data. The large online forum is home to a variety of Bitcoin-related topics, where users actively engage in conversations by posting comments and forming threads[ 6 , 29 ].

The bulletin boards on the Bitcoin online forum are largely comprised of four different sections. Each section consists of three to five sub-sections. The threads of comments and replies posted from 1 December , when Bitcoin started to sweep the globe, until 21 September were crawled.

Each thread, including the topics and all relevant replies, the time when such posts appeared on the forum, the number of replies posted, and view counts were crawled as well. Duplicate sentences were removed from the replies that quoted earlier posts or replies prior to crawling.

We collected data in a legitimate manner, in compliance with the terms and conditions. Moreover, the collected data did not involve any personally identifiable information.

Furthermore, we used Coindesk to crawl the daily Bitcoin price and the number of transactions for the abovementioned sample period See Table 1. In addition, we reinforced the learning model by crawling the widely used Google Trend data and Wikipedia usage data. Google Trend shows the search interest in a certain keyword on a scale of 1 to based on its search volume on Google for a certain sample period. Google Trend data is widely used to analyse data and phenomena in multiple disciplines[ 30 — 34 ].

The Wikipedia usage volume data is based on the page views of a certain keyword on a certain day, and broadly used in many analytical studies on data or Internet phenomena[ 34 — 36 ]. Table 1 outlines the arrangement of opinion and market data crawled. Our intention was to extract significant keywords used in Bitcoin transactions from the aforementioned crawled data.

Therefore, we conducted topic modelling on every user comment to extract the keywords, which were in turn subjected to kernel density estimation for score rating. Our main goal was to extract quantitative features related to diverse characteristics from documents see Fig 2. We considered the feature value as the degree of relevance for a feature.

In detail, the feature value represents the extent to which a document has a particular characteristic. For example, sentiment analysis concerns one such quantitative feature, or the extent to which a document is positive or negative.

We generalised this idea to various other user-defined characteristics. Examples of such characteristics include the extent to which a document is related to finance, immigration, and family issues.

In particular, we built a lexicon, i. In this study, we considered a characteristic to be a concept describing a particular phenomenon or object, and defined a concept by constructing a set of keywords, whose meanings were relevant. Concepts can play an important role in document analysis in diverse fields. That is, one can build useful domain-specific concepts in economics, politics, and social sciences and define the characteristics of documents with respect to these concepts.

Here, the concept building process was composed of two steps: 1 the initial construction of a relevant keyword set, followed by its 2 user-interactive expansion. In order to facilitate the first step, we provided a user with the initial sets of coherent keywords obtained with two different techniques.

The first technique we used was topic modelling, which algorithmically computes those representative keywords emerging from a document corpus.

The user can then select some of them as an initial word set for their own concepts. As the other method to provide initial keywords, we computed the representative keywords from the centroid vectors obtained by k-means clustering on word embedding vectors[ 37 ]. Once a user formed an initial, small-sized lexicon for a particular concept, the second step was to interactively expand it by using a recently proposed visual analytics system named ConceptVector.

Based on the initial lexicon given as user inputs, ConceptVector recommended potentially relevant keywords to enable users to easily add a subset of them to the lexicon. As the lexicon expanded, ConceptVector adjusted the recommended keywords that match the semantic meaning of the concept. The topic modelling approach we used to extract representative keywords emerging from a document corpus is non-negative matrix factorisation, where the non-negativity allows users to interpret the value from factor matrices as the relevance score of a word or a document to a particular topic as mentioned above.

In particular, we constructed a document-term matrix A from the 17, forum articles and , user comments collected from the Bitcoin forum See Table 1. We then applied the topic modelling to each so as to extract the different topic sets and their representative keywords across different dates. The mathematical details of this process are as follows. Given a document-term matrix where m is the number of articles and n is the dictionary size, Non-negative Matrix Factorization NMF approximately factorises it into two matrices and , where d represents the number of topics 50 in our study , e.

The columns in the resulting matrix W correspond to different topics and the keywords corresponding to the dimensions of the k largest value in each column function as the representative keywords of the topic. We proposed two types of concepts in the system. A unipolar concept represents exactly one concept such as crude oil and immigration. A bipolar concept has two polarities that oppose each other, e. In the case of building a concept, the system has positive, negative, and irrelevant word sets.

When a user provides a word as an input, the system provides 50 recommended words that are potentially relevant to the seed word. We then automatically sorted the recommended words into five clusters, using the k -means clustering, to gather closely related terms into one group. Once the lexicon of a concept is created by user interactions, the document rating process utilises the concept built in the process above.

Because of the lack of expression resulting from the limited number of words a person could manage, we applied the kernel density estimation KDE in the word rearranging phase.

Prior to the KDE, the concept had a limited number of descriptive terms for a characteristic, which resulted in a lack of expression and description. Therefore, the KDE served for the probabilistic smoothing over every word. This smoothing process is the most important procedure for document analysis since the score rating process cannot consider synonyms or closely related words that also represent a specific concept.

Based on the assumption that the input terms describe the concept sufficiently well, we constructed a kernel that exerts influence on the entire vocabulary. ConceptVector adopts a Gaussian kernel as described below. The conditional probability of a keyword z for a class c can be computed as below: 2 which can also be seen as the relevance score to each class.

Since our final goal was to obtain scores by taking all classes into consideration, we rated a concept in view of all classes. We calculated the bipolar rating as below: 3 4. The Granger causality test is based on the supposition that if a variable X causes Y, then any change in X will methodically happen before any change in Y[ 17 , 22 , 38 ]. As shown in past research, slacked estimations of X display a measurably noteworthy connection with Y[ 17 , 22 , 38 ].

The Political Geography and Environmental Impacts of Cryptocurrency Mining

In December , 88 percent of all remote code execution RCE attacks sent a request to an external source to try to download a crypto-mining malware. These attacks try to exploit vulnerabilities in the web application source code, mainly remote code execution vulnerabilities, in order to download and run different crypto-mining malware on the infected server. RCE vulnerabilities are one of the most dangerous of its kind as attackers may execute malicious code in the vulnerable server. Have you ever wondered what kind of malicious code attackers want to execute? The answer in most cases is — any code that earns the attackers a lot of money with little effort and as quickly as possible. During a recent research project, we saw an extremely large spike of RCE attacks. A remote code execution vulnerability allows attackers to run arbitrary code on the vulnerable server.

Why does Bitcoin need more energy than whole countries?

Close panel. Press Enter. Central bank-backed digital currencies, such as the potential digital euro and digital yuan, may become a reality in the coming years. Unlike cryptocurrencies such as Bitcoin and Ethereum, these currencies promise less volatility and greater security. In addition, they will have the support of their respective monetary institutions, responsible for ensuring financial stability. The ECB is proceeding with caution and it is believed that the first studies and tests could be carried out in mid One possibility is putting into practice formulas based on blockchain technology , the same one used by cryptocurrencies such as bitcoin and ether. This would allow Europe to have tools that allow for greater transparency and monitoring of information, transactions and movements carried out, according to the BBVA Research report ' Digital currencies issued by central banks: features, options, pros and cons. Unlike these two cryptocurrencies, which also have DLT distributed ledger technology , officially backed digital currencies will be issued centrally and will be backed by their central banks. Referring to cryptocurrency mining by users.

Electricity needed to mine bitcoin is more than used by 'entire countries'

You might be using an unsupported or outdated browser. To get the best possible experience please use the latest version of Chrome, Firefox, Safari, or Microsoft Edge to view this website. If Bitcoin were a country, it would rank in the top 30 worldwide for energy use. The blockchain technology that underlies it, meanwhile, could be the key to a greener future. All you have to do is point and click or tap on your smartphone to buy and sell the cryptocurrency.

Visualizing the Power Consumption of Bitcoin Mining

Learn more about Climate Week, read our other stories , and check out our upcoming events. Image: fdecomite. Because some bitcoin investors have become millionaires overnight, more and more people are intrigued by the possibility of striking it rich through investing in cryptocurrencies like Bitcoin. A cryptocurrency is a virtual medium of exchange that exists only electronically; it has no physical counterpart such as a coin or dollar bill, and no money has been staked to start it. Cryptocurrencies are decentralized, meaning that there is no central authority like a bank or government to regulate them.

Bitcoin and Potosí Silver: Historical Perspectives on Cryptocurrency

Bitcoin mining, in and of itself, is not harmful and involves using a computer to solve difficult mathematical equations for the user to earn bitcoin. The user earns bitcoin by verifying transactions on the blockchain, which is a digital ledger—similar to a bankbook—that keeps track of all the transactions of a given cryptocurrency. Each time a hash is solved, the user who solves it earns bitcoin. However, to solve the math problems, a computer has to run nonstop, expending a lot of central processing unit CPU power. This takes a lot of electricity. Bitcoin mining uses malware. Hackers have written malware with the ability to access your computer and use its resources to mine bitcoin and other cryptocurrencies.

New Research: Crypto-mining Drives Almost 90% of All Remote Code Execution Attacks

Bitcoin is the newbuzz word in computing these days. It holds the promise of making fast money and is a lucrative temptation for those who like to play big. Bitcoin is a transaction protocol for digital currency transactions and is the name of the crypto currency.

Crypto Mining: Definition and Function Explained

Bitcoin mining — the process in which a bitcoin is awarded to a computer that solves a complex series of algorithm — is a deeply energy intensive process. Bitcoin mining — the process in which a bitcoin is awarded to a computer that solves a complex series of algorithms — is a deeply energy-intensive process. Miners are rewarded in bitcoin. But the way bitcoin mining has been set up by its creator or creators — no one really knows for sure who created it is that there is a finite number of bitcoins that can be mined: 21m. The more bitcoin that is mined, the harder the algorithms that must be solved to get a bitcoin become. Now that over

Bitcoin miners align with fossil fuel firms, alarming environmentalists

Riot Blockchain, Inc. We are focused on expanding our operations by increasing our Bitcoin mining hash rate and infrastructure capacity. Riot believes the future of Bitcoin mining will benefit from American operations and endeavors to be the driver of that future. Our Bitcoin mining operations include both Whinstone U. Whinstone U. Riot currently has a deployed hash rate capacity of 3.

Bitcoin, the first cryptocurrency , was created in to cut out the middle-man from the commerce of digital assets. These accountants compete against each other to record each new payment in the decentralised ledger and, in exchange, receive new bitcoins. The catch? This process, known as mining, has become so competitive that nowadays it can only be done with powerful computers whose energy consumption, as a whole, surpasses that of many countries.