Bitcoin wikipedia frank

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Craig Steven Wright

Try out PMC Labs and tell us what you think. Learn More. Cryptocurrencies have recently received large media interest. Especially the great fluctuations in price have attracted such attention.

Behavioral sciences and related scientific literature provide evidence that there is a close relationship between social media and price fluctuations of cryptocurrencies. This particularly applies to smaller currencies, which can be substantially influenced by references on Twitter. Here, we show that fluctuations in altcoins can be predicted from social media. In order to do this, we collected a dataset containing prices and the social media activity of altcoins in the form of , tweets over a timeframe of 71 days.

The containing public mood was then estimated using sentiment analysis. To predict altcoin returns, we carried out linear regression analyses based on 45 days of data. We showed that short-term returns can be predicted from activity and sentiments on Twitter. Until a few years ago, cryptocurrencies hardly received any public attention.

Only few people had heard about Bitcoin, if at all. It seemed that all of a sudden, Bitcoins were perceived as the currency for illegal purposes.

Today, cryptocurrencies increasingly assert a place in the awareness of society and the public. This is accompanied by an increasing research interest. Thus, a rising number of studies have placed their focus on Bitcoins and cryptocurrencies in general. Two questions that many of these studies address are: How are returns of cryptocurrencies determined and is it possible to predict them?

As for traditional commodities, there is reasonable evidence to suggest that cryptocurrency returns are somehow connected to public perception. Cryptocurrencies tend to be highly volatile which makes them sensitive to external factors.

In such a scheme, chosen cryptocurrencies are purchased in small, inconspicuous amounts in a certain time range. In total, these purchases then add up to a substantial amount. Secondly, these cryptocurrencies are then heavily promoted via social media, which leads to purchases by other parties.

The corresponding price increases provides the persons initiating the scheme with very high returns. Altcoins are easier to influence and are thus of special interest in this study. Another factor that suggests a connection between cryptocurrency returns and social media is based upon theories of behavioral sciences. Social media platforms such as Twitter can be seen as a collective indicator of thoughts and ideas but also of public mood [ 4 ].

Positive mood states lead to more optimism towards investment decisions [ 5 ]. Thus, social media might be a good prediction indicator for investments in cryptocurrencies, of which the extents are reflected in their prices. While there are all sorts of social media information available, we believe that it is especially the short-term variability of Twitter that makes the platform a very suitable indicator for short-term predictions.

Furthermore, unlike services such as Google Trends, Twitter does not solely provide information about activity but also about the associated mood.

Fig 1 illustrates the price and the social media activity which is represented by the number of tweets for the cryptocurrency PinkCoin. At first glance, one would suspect a connection. In the scope of this work, we wanted to pay closer attention to these indications.

Over a time range of 71 days, we collected a dataset covering altcoins. In terms of the scientific literature, this is an unparalleled achievement. Based upon this data, we ran linear regression analyses in order to create short-term prediction models. The aim of these models was to analyze whether it is possible to predict altcoin returns based upon social media activity. The activity was measured by the number of tweets that referred to a certain altcoin and the sentiment that they contained.

We therefore hypothesized that it is possible to predict the return of an altcoin based upon the number of tweets that are referring to it and the sentiment contained in said tweets. The scientific interest in cryptocurrencies is relatively new. However, there is some related work in existence.

Some papers deal with Bitcoin users [ 6 — 8 ], Bitcoin in general [ 2 , 9 — 13 ] or the market dynamics of cryptocurrencies [ 14 — 16 ]. Fewer papers have have analyzed the relationship between cryptocurrency prices or returns and social media activity. While some authors have based their analysis on online forums [ 17 , 18 ], Google Trends and Wikipedia [ 19 ] or social media platforms like Reddit [ 20 ], others have used data provided by Twitter [ 21 ].

Authors such as Garcia and Schweitzer [ 22 ] have even created a framework to bundle several social and economic signals in order to predict the return rates of Bitcoins. Kristoufek [ 23 ] has investigated the most frequently claimed drivers of the Bitcoin price. Several authors argue that Bitcoins, consequently cryptocurrencies in general, have mainly been addressed in a speculative frame [ 7 , 24 , 25 ]. Therefore, one could say it may be justified or even more appropriate to treat cryptocurrencies much like stocks rather than fiat currency.

Both studies have used tweets including certain expressions of mood. While Bollen et al. While we were assessing the above-mentioned literature, we came across a few things that stood out. No cryptocurrency-related paper, except for one, has considered other cryptocurrencies than Bitcoin in their analyses.

This might be due to the superior interest in Bitcoin, or there may even be a more trivial reason: many people may not even know of the existence of other cryptocurrencies. Secondly, all cryptocurrency-related papers have only focused on daily prices. Cryptocurrencies tend to be extremely volatile. Therefore, considering intraday prices would allow a more granular, detailed picture. It is also notable that among all papers the sentiment analysis approaches vary.

Some authors [ 4 , 21 ] have assigned tweets a class positive, neutral, negative and have used the number of tweets of each for their analysis. Other authors such as Kim et al. There are many more studies in existence that have attempted to predict stock market prices using different factors. Especially the prediction using public sentiments seems to be of superordinate interest. For example, some authors have also used sentiments on Twitter [ 28 — 33 ] whereas others have used sentiments from stock message boards [ 34 , 35 ].

However, the scientific interest in cryptocurrencies is relatively new and therefore, there are only a small number of studies. This is understandable because cryptocurrencies as we know them today started to exist with the appearance of Bitcoin, which is less than ten years ago [ 36 ]. Based on these grounds, we aimed to expand the field by increasing the quantity of cryptocurrencies included in our analyses.

In order to do this, we collected data of the largest altcoins. We also paid attention to the emotional value of tweets pertaining to cryptocurrencies. We showed how Twitter activity and sentiment could be used for some currencies to predict altcoin returns.

This is the first time that small cryptocurrencies have been rigorously investigated for their prediction potential based on Twitter. To further support this growing field, we share the collected dataset with the scientific community in the Supporting Information S1 File. For this work, we collected altcoin prices and social media activity of altcoins over a period of 71 days in total. Both, prices and social media activities were updated every three hours to also allow for short-term analysis.

These datasets are not continuous but split into two time periods. The first period starts on March 21 and ends on May 5, and thus covers 45 days. The data collected in this range represented the training set. The second period covers 26 days and starts on May 9 and ends on June 4, The data obtained in this range functioned as the test set. This set was used to evaluate the models based on the training set. The ratio of these sets is around 1.

In total, our datasets include prices for different altcoins and , tweets referring to them. All datasets were collected in a legitimate manner, fully complying with the terms of service of the sources in use. The sources of our data will be explained in the next sections. Using this, one can directly crawl Twitter data including usernames, hashtags, tweets, the associated timestamp of tweeting and the number of retweets. In our case, a query referred to a certain altcoin.

This was done by using the official currency codes. We collected tweets that were retweeted just as we collected tweets that point to several altcoins.

This was to ensure, that the picture that was presented to the twitter community, gets reflected in our data corpus. Due to restrictions placed by Twitter it was only possible to gather a random sample of up to tweets per query resulting in updated social media activity every three hours for each altcoin.

Queries were processed at three-hour intervals and contained up to tweets that had been posted since the last query. For each query, we saved all tweets and the total number of tweets.

In order to extract the sentiment expressed in a tweet, we applied VADER, a valence-based sentiment analysis model that especially addresses the analysis of social media text such as tweets [ 38 ]. Tweets often contain language variation and show the frequent usage of emoticons, abbreviations or slang [ 39 ]. These special characteristics lead to particular requirements when it comes to sentiment analysis in order to avoid information loss.

VADER meet these demands by relying on lexicons that contain Western-style emoticons and sentiment-related acronyms [ 38 ]. However, there are several other sentiment analysis approaches in existence that will lead to a similar outcome. When applying VADER for sentiment analysis, four different scores are computed: positive, neutral, negative and compound.

The positive, neutral and negative scores are portions or segments of text that are matched to their respective group. These segments, when added to each other, should total a sum of 1.

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Inflation will remain high at the start of but will fall later on, especially towards the end of the year, Chief Economist Philip R. Only 12 counterfeits were detected per 1 million genuine banknotes in circulation last year, which is a historically low level. She also tells us why we should never forget. Why does monetary policy matter? Because it has a direct impact on interest rates and an indirect impact on inflation, the economy and employment. We are always working to improve this website for our users. To do this, we use the anonymous data provided by cookies. Learn more about how we use cookies.

Matt Odell

bitcoin wikipedia frank

This article has been identified as a stub , meaning that this page is lacking in necessary information. You can help out Euphoria Wiki by expanding it to improve the quality. Katherine "Kat" Hernandez is a main character in the first and second seasons of Euphoria. Kat had a peaceful and relatively good childhood, without any trauma or complications. During the 6th grade, she got in her first relationship with a boy named Daniel , who treated her kindly, like texting her before going to bed or waiting for her outside of school every day, so they could walk together through the hallway while holding hands.

Bitcoin is the most disruptive invention since the Internet, and now an ideological battle is underway between fringe utopists and mainstream capitalism. The film shows the players who are d

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Almost a decade after the introduction of Bitcoin, there is a lot of hype about the blockchain technology on which cryptocurrencies such as Bitcoin are based. Some claim the technology will revolutionise commerce; others are more critical in their predictions. But the technology behind blockchain remains a mystery to many people. If that still sounds like gibberish to you, there is a popular application that shares the philosophy of the blockchain technology that can help you understand how it works: Wikipedia. Despite being based on a central database, Wikipedia is decentralised in the sense that the ability to add information is completely open and public. This freedom to add information to the database, or ledger, and the freedom to access the full history of all previous changes, is similar to a blockchain.

The Race to Replace Bitcoin

Try out PMC Labs and tell us what you think. Learn More. Cryptocurrencies have recently received large media interest. Especially the great fluctuations in price have attracted such attention. Behavioral sciences and related scientific literature provide evidence that there is a close relationship between social media and price fluctuations of cryptocurrencies.

An epic battle between two bitcoin contenders grips the crypto world. a tax partner in the San Jose CPA firm Frank, Rimerman LLP.

Education: A. Burke brings 42 years of experience as a business trial lawyer in major national law firms, representing both plaintiffs and defendants in a diverse range of business disputes in federal and state courts in 26 states, China and Canada. He helps parties resolve their conflicts in a fair, cost-effective way through creative approaches and relentless follow-up. He likes to get to know the parties and hear their perspectives.

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  2. Mezir

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