1. Introduction

Social media discussion forums involve hundreds of thousands of subscribers (Comben and Rivet, ; Knittel and Wash, ) and, as in the case of Reddit subreddits, bitcoin investors forum 100, may use moderators to ensure focus on a specified theme (r/Bitcoin, ; r/ethereum, ). In this paper, we introduce a framework for analysing the association between changes in social media discussions and shifts in the movement of a related cryptocurrency price series. We evaluate this framework through the insights it provides when applied to bitcoin and ether prices across &#x; For cryptocurrencies, social media discussions are particularly relevant (ConsenSys Media, anno 1800 wie geld verdienen Revealing Reality, ) and, during &#x;18, changes in bitcoin investors forum 100 price movement were particularly extreme (see Figure 1). Potential causes of shifts in the price series are discovered in social media discussions that either have a one-off, major effect, bitcoin investors forum 100, including unpredictable black swan (Taleb, ) events, or have a consistently recurring effect on price.

Figure 1. Comparison of ether and bitcoin US Dollar price series (1 January to 14 May ). Bitcoin series is in blue and the price is given by the left axis. Ether series is in light green and the price is given by the right axis. The horizontal line represents the identified support or resistance price level which was US Dollars for ether and 6, US Dollars for bitcoin. The labelled dates on the x-axis are dates where there was a bitcoin or ether local maxima or minima, or where the horizontal line was breached.

If an event occurs as price changes, that event could be driving the change in price, but a reasonable alternative explanation is that the event is in response to the change in price. To exclude the latter possibility, cause must come before effect as the future cannot affect the past (Bradford Hill, ; Granger, ; Ioannidis, ). Hence, the event must precede the price change, and such events, therefore, may be predictive. Previous literature has focussed on models to predict the cryptocurrency price. For instance, seven studies have found a higher Google search volume to precede price increases (Georgoula et al., ; Matta et al., ; Polasik et al., ; Li and Wang, ; Abraham et al., ; Kjærland et al., ; Liu and Tsyvinski, ), and ElBahrawy et al. () found that the bitcoin investors forum 100 of Wikipedia views on cryptocurrency pages could be used in a trading strategy to generate higher investment returns up until January

However, establishing a predictive relationship does not prove a causal link because of confounding bias (Pearl et al., ). That is to say if one event occurs before another, both may be the symptoms of a third factor changing (Pearl et al., ) or there may have been a catalyst unique to that dataset without which the causal link ceases (Rothman, ). For example, higher Google search volumes may occur before higher prices because positive news events drove people to both search on the internet to find out more and to buy the cryptocurrency (Kristoufek, ; Liu and Tsyvinski, ). Hence, Kristoufek () established that a positive correlation relied on including days in the dataset when the price was high and positive news events common. However, negative news items could also lead people to Google search but instead result in lower prices, resulting in a negative correlation. Consistent with this, Garcia et al. () found a negative correlation and Urquhart () no predictive association between Google searches and price, bitcoin investors forum 100. Confounding bias remains an issue even when applying non-parametric approaches to learning causal networks (Maathuis et al., ; Runge et al., ); to construct these networks, assumptions are also required regarding the conditional independence between variables (Dablander and Hinne, ).

Ideally, experiments would be carried out to reduce the risk of confounding bias (Pearl et al., ; Rosenbaum, ), but for cryptocurrencies we bitcoin investing canada 55 only observational data. Although observational data cannot prove that a candidate caused a change it can provide evidence that favours this explanation over confounding bias (Pearl et al., ; Rosenbaum, ). It is in this context that healthcare epidemiologists often operate to find the underlying causes of disease, as, for instance, with the link between smoking and lung cancer (Cornfield et al., ; Rosenbaum, ).

Our approach (see Figure 2) is to filter words from social media text, group words of similar meaning to identify the underlying concepts, bitcoin investors forum 100, and then to apply quantitative causality criteria. We then examine the context of the delineated concepts and evaluate the coherence of suggested causal links with known facts (Bradford Hill, ). Healthcare epidemiology literature suggests bitcoin investors forum 100 distinct approaches to constructing bitcoin investors forum 100 quantitative causality criteria.

Figure 2. The causality framework. This evaluates evidence for or against an event and/or concern on social media having an impact on price. The framework begins in the box labelled Data Preparation. The mono-phase analysis follows the route on the left and the multi-phase analysis follows the route on the right; differences in approach are indicated by coloured text. The process terminates in the box labelled Coherence with Known Facts .

The first approach bitcoin investors forum 100 the strength of the association to support a causal link (Bradford Hill, ; Rosenbaum, ). The larger the increase in the candidate cause and the greater the effect, the more any third, unconsidered, confounding variable would have to affect both for the association to be spurious and not indicative of a causal relationship (Cornfield et al., ; Grimes and Schulz, ; Rosenbaum, ). This is applicable to identifying rare, unpredictable black swan events that have a one-off influence on a single, major phasic shift in the price series. In the mono-phase analysis (see Figure 2) we focus on the major change in the price series which is the shift in movement from the phase of rising prices before to the phase of falling prices after the all time high price. We filter for words that were statistically significantly higher in frequency in the latter phase of falling values. The causality criteria used are: frequency is more than three-fold higher (Grimes and Schulz, ) in the phase of falling prices than the phase of rising prices, and frequency is higher within the 24 h before the maximum price. We use a cut-off that the concept must be more than three-fold higher in frequency to reduce the risk that the detected association is spurious. This is consistent with recommendations in the epidemiology literature regarding the definition of what constitutes strong support for causation (Grimes and Schulz, ).

The alternative approach places value in relationships that consistently recur despite a changing context (Bradford Hill, ; Ioannidis, ). The more an observed association recurs across different contexts, the more likely any unobserved variables would have bitcoin investors forum 100 in value and impact, bitcoin investors forum 100, and so the less likely that the observed association is due to some unobserved variable driving both candidate cause and effect, bitcoin investors forum 100. This approach can detect potential causes with a recurring effect on the price series. In the multi-phase analysis (see Figure 2), we filter for words where daily frequency was statistically significantly different comparing all phases of rising values with all phases of falling values. A concept captured a potential recurring cause of rising values if its frequency was higher in every phase of rising values compared with the previous phase and higher within the 24 h before each phase of rising values. Concepts reflecting potential causes of falling values have a higher frequency in every phase of falling values compared with the previous phase and a higher frequency within the 24 h before each phase of falling values.

Our results support the existence of both causes with a one-off effect, which could be attributable to black swan events, and causes with a consistently recurring effect on price. Most of the causes differed between bitcoin and ether which is consistent with the difference in timing of the phases and all time high price (see Figure 1), and their different functions (Burnie et al., ).

2. Materials and Methods

An overview of the methodology is provided in Figure 2.

Data Preparation

Dataset

The dataset extended from 1 January to bitcoin investors forum 100 May and included: Reddit submissions text sourced using the Pushshift API (Baumgartner, ), the US Dollar bitcoin price from the Bitcoin investors forum 100 API of Blockchain Luxembourg S. A. () and the US Dollar ether price from Etherscan ().

For Ethereum, the largest (Comben and Rivet, ) subreddit r/ethereum hadsubscribers on 14 May (r/ethereum, ) and was moderated by Vitalik Buterin, the Creator of Ethereum (Alvarez, ). Following this forum&#x;s guidelines (r/ethereum, ), its text was combined with that from r/ethtrader and r/EtherMining. Together, these had the most submissions containing the term ether or eth among Ethereum-specific subreddits (Baumgartner, ) and have collectively been described as the most important subreddits (Comben and Rivet, bitcoin investors forum 100, ). For Bitcoin, we used subreddit r/Bitcoin, which has been recommended because of the number and activity of its users compared with alternative online communities (Knittel and Wash, ); this community had over million subscribers as of (UTC) on 23 August (r/Bitcoin, ).

Dividing the Price Series Into Phases

The price data was divided into phases using local maxima and minima to define the boundaries. A date represented a local maximum if the price was higher than on any other date 28 days (4 weeks) before and after. That date was a local minimum if bitcoin investors forum 100 price was instead lower than on any other date 28 days before and after. Phases terminating just before a local maximum were rising price phases, those ending just before a local minimum were falling price phases. Sometimes there were several consecutive minima with the last value being the lowest; we ignored all such minima except the last, lowest value.

The length of the window was specified at 28 days before and after because a longer window risked merging rising and falling price phases. For example, examining bitcoin, the day window delineated a phase where bitcoin prices fell 65% from the all time high price on 16 December to 5 February (see Figure 1 and Figure S1). Doubling the length of this window to 56 days would have enlarged this phase of price movement to include the subsequent 70% increase in prices from 5 February to 5 March Using shorter time windows would have reduced the size of the price phases, limiting the amount of data available when applying Wilcoxon Rank-Sum Tests to filter words in the mono-phase analysis (described in section ). This would have reduced the power of such tests (Bridge and Sawilowsky, ).

As bitcoin prices rose acrossthere were brief phases where bitcoin prices reversed upon reaching round values. This occurred at 1, US Dollars ( to from 3 to 24 March ); 3, US Dollars ( to from 11 June to 16 July ); and 5, US Dollars ( to from September ). Bitcoin investors forum 100 sell at round values that represent a large return on their investment to prevent losing this return to subsequent volatility, even if their view of the cryptocurrency is unchanged (Chen, ). Therefore, we incorporated these phases into the overall rising price phase.

When technical traders believe that a certain price level is a support or resistance level, they will buy (pushing prices bitcoin investors forum 100 as prices fall to that support level and sell (pushing prices down) as prices rise to that resistance level (Murphy, ). When prices approach a round-valued price this can drive reversals in trend even if opinion of the cryptocurrency is otherwise unchanged (Shiller, ; Westerhoff, ; Aggarwal and Lucey, ; Dowling et al., ). Phases where the connect between price and non-price events and concerns is weak were excluded.

Inthe ether price rose to US Dollars (12 June), fell to near US Dollars ( US Dollars, 16 July ), then rose again to US Dollars (1 September ) (Figure 1). This supports a US Dollar price resistance level identified bitcoin investors forum 100 the media at the time (Bamburic, ; Wilmoth, ). Hence, we remove from analysis the phase from 12 June (where the barrier was first neared) to before 23 November (when the barrier was exceeded).

Inthe bitcoin price fell to US Dollars (29 June ), recovered and tested the barrier again at (14 August ). Hence the US Dollar support level has been described as a crucial test (Cuthbertson, ). We remove from analysis the phase from 29 June to before 15 November (when prices finally fell below the barrier).

After attaining a local minimum in mid-Decemberbitcoin investors forum 100, neither the bitcoin nor ether price fell further. This point thus marks the end of the &#x;18 price cycle which is the focus of this paper&#x;s analyses, and so the last phase of data analysed ends mid-December for both cryptocurrencies (14 December for Ethereum and 15 December for Bitcoin).

Text Preparation

Reddit submissions were processed as detailed in the Supplementary Methods (see section ), in the Supplementary Data Sheet 1. Table 1 uses examples to illustrate the different datasets generated during the processing of the text. Blank, duplicate bitcoin investors forum 100 automated submissions were removed, text of synonymous meaning was standardised and text not relating to words deleted. Each submission was converted from a string of text into a list of words; see columns (A) and (B) in Table 1 for examples.

Table 1. Examples illustrating the different datasets resulting from extracting daily word frequencies from the original Reddit submissions.

Measuring Frequency

With each submission represented as a list of words, the number of submissions across a defined time period that contained each word could be counted. This was then divided by the total number of submissions such that the frequency or popularity of a word was the proportion of submissions across a defined time period that contained that word at least once. Extending to groups containing multiple words, frequency was the proportion of submissions containing at least one word from that group. Daily frequency referred to the proportion of submissions containing a word or a word from a group on each day. Following the sources on price data (Blockchain Luxembourg S. A., ; Etherscan, ), a day was specified to be from on a given day to before the next date (UTC). Table 1 provides bitcoin investors forum 100 daily frequency data for the word bitcoin.

Mono-phase Analysis

Filter Words

One-tailed Wilcoxon Rank-Sum Tests (SciPy package version ) and a Bonferroni-corrected p-value threshold of 1% were applied to filter for those words where the daily word frequency tended to be higher in the phase after the all time high price compared with before, bitcoin investors forum 100. The Wilcoxon Rank-Sum Test was used as a non-parametric equivalent to the t-test that is less sensitive to extreme outliers (Bridge and Sawilowsky, ; Wild and Seber, ). The Bonferroni correction (dividing the p-value threshold by the number of tests) was applied to account for a multitude of tests being run for each word (McDonald, ). Prior to this, extremely rare words in or less submissions were removed.

Identify Concepts

From the delineated words, concepts were derived that consisted of one or more words that shared a similar meaning. This followed Burnie and Yilmaz (a) and used Python packages gensim (&#x;eh&#x;ek, ) version and NetworkX (NetworkX, ) version Firstly, word2vec models (Mikolov et al., a,b) were trained using the processed text from all submissions (see section ). The trained word2vec model was used to convert each delineated word (found in section ) into a numeric vector, bitcoin investors forum 100. A network was constructed where two words were connected only if the cosine similarity between their vectors exceeded a threshold. The cosine similarity between a pair of vectors provided a measure of how similar the pair of words were in meaning (Mikolov et al., a,b). Groups of connected words were merged into single concepts (such as cardano /eo /iota /rippl /stellar /tron ) whilst words unconnected with any other word (korea ) were treated as concepts consisting of only one word, bitcoin investors forum 100. The optimisation of this methodology followed Burnie and Yilmaz (a).

Apply Causality Criteria: Strength and Cause Before Effect

Mono-phase concepts were more than three-fold bitcoin investors forum 100 in popularity (Grimes and Schulz, ) across the phase after the all time high price compared with the phase before, and increased in frequency before the shift in phase. To determine if frequency rose before the shift, we examined 1, 2, 3 h, and so on, bitcoin investors forum 100, up to 24 h before the shift and evaluated whether the proportion of submissions containing the concept within any of these windows was higher compared with all the submissions in the same phase but before that window.

Multi-Phase Analysis

Filter Words

Two-tailed Wilcoxon Rank-Sum Tests (SciPy package version ) and a Bonferroni-corrected p-value threshold of 1% were applied to extract those words where the daily word frequency tended to be higher or lower comparing all phases where prices rose with all phases where prices fell. Prior to this, extremely rare words in or less submissions were removed.

Identify Concepts

Words more frequent as prices rose were split from those more popular as prices fell. As in sectioneach set of words was converted into a set of concepts: rising-price concepts consisted of words higher in frequency as prices rose and falling-price concepts consisted of words more frequent as prices fell.

Apply Causality Criteria: Consistency and Cause Before Effect

Rising-price, multi-phase concepts were rising-price concepts that rose in frequency with every shift to rising prices and within the 24 h before every shift to rising prices. Falling-price, multi-phase concepts were falling-price bitcoin investors forum 100 that rose in frequency with every shift to falling prices and within the 24 h before every shift to falling prices. We removed from the analysis any concept that consistently rose in popularity across every shift in price, independent bitcoin investors forum 100 whether prices were rising or falling, as any rise bitcoin investors forum 100 popularity could have been an artefact of the long-term trend.

Context of Concepts

For each mono-phase and multi-phase concept, we found the top five most common words occurring in submissions containing at least one word from that concept. This excluded words that did not aid in the interpretation of the concept. Further details and a list of words excluded are available in section of the Supplementary Methods, in the Supplementary Data Sheet 1.

3. Results

Comparison of Bitcoin and Ethereum Price Phases

Both the bitcoin and the ether price rose to an all time high as becameto then oscillate with an overall decline in value until mid-December (see Figure 1). Bitcoin investors forum 100 was a disparity in the timing of the all time high price for bitcoin (16 December ) and ether (13 January ).

It appears that different price levels acted as barriers at different times. Whilst bitcoin prices rose acrossether prices reverted upon nearing US Dollars (Bamburic, bitcoin investors forum 100, ; Wilmoth, ) (12 June and 1 September ), bitcoin investors forum 100, only increasing above this level after five months. Whilst ether prices fell from 5 May to mid-Decemberbitcoin prices recovered upon falling to 6, US Dollars (Cuthbertson, ) (29 June and 14 August ) and only fell below this level after four months.

Based on local extrema (see Figure S1) and price barriers, we demarcated six phases of price movement with ether and eight with bitcoin (see Table 2). Table 2 further shows which of these phases were used in order to compare daily word frequencies so as to filter words (see sections and ). Descriptive statistics for the different phases are provided in Table S7.

Table 2. For each phase in the cryptocurrency price series: the date range, price movement, overall percentage increase and in which Wilcoxon Rank-Sum Test that phase was used.

Mono-phase Concepts and Their Context

Ether prices rose % (phase 3) to an all time high price on 13 January before falling 73% (phase 4). Only feb met the criteria for a mono-phase concept and was excluded as it reflected the timing of phase 4.

Bitcoin prices rose 1,% to an all time high price on 16 December during phase 1 and then fell 65% (phase 2). Ten mono-phase concepts rose more than three-fold with this shift to falling prices and increased within the 24 h period before entering the falling price phase (see Figure 3). The words occurring with these concepts (see Table 3) suggested three themes: regulatory bans (korea and minist /ministri ); concerns over whether to sell bitcoin or switch to an altcoin (cardano /eo /iota /rippl /stellar /tron ; airdrop ; binanc /hitbtc ; hashflar ; and discord ); and discussion of the practicalities of transacting bitcoin (batch, bech32 and changelli ), bitcoin investors forum 100. Two further concepts (merri and christma /holiday /xmas ) also met the mono-phase criteria but were excluded because these were most likely due to the timing of phase 2, which began on 16 December

Figure 3. Frequency data for mono-phase concepts in the case of Bitcoin. This shows the percentage of all submissions containing the concept in phase 1 (light green) and phase 2 (blue).

Table 3. Top five words occurring with each Bitcoin mono-phase concept in phase 2.

The context of the altcoin group (cardano /eo /iota /rippl /stellar /tron ) reflected the contexts of each cryptocurrency named. Three of these six cryptocurrencies increased more than three-fold in the proportion of submissions from phase 1 to 2: Cardano rose %; Tron %; and Ripple (represented by rippl ) %. We examined the top five words occurring with each of Cardano, Tron and Ripple and the altcoin group (cardano /eo /iota /rippl /stellar /tron ) and found in each case they were discussed with: ethereum, buy, price (price or US Dollars) and another cryptocurrency (bitcoincash or rippl and verg in the case of Tron). Further details in Table 4.

Table 4. Top five words occurring with each of Cardano, Tron and Ripple (rippl ) compared with the Bitcoin mono-phase concept cardano /eo /iota /rippl /stellar /tron in bitcoin investors forum 100 2 of the bitcoin price series.

We also split up the concept binanc /hitbtc which combines two different cryptocurrency exchanges: Binance and HitBTC. Interest in Binance rose % in frequency compared with only % for HitBTC. The context in which binanc was used was similar to the concept binanc /hitbtc, with the top ten words being shared and the top three words having the same ranking (coinbas, US Dollar mentions and send). Further details in Table 5.

Table 5. Top ten words occurring with Binance (binanc ) compared with the Bitcoin mono-phase concept binanc /hitbtc in phase 2 of the bitcoin price series.

Multi-Phase Concepts and Their Context

With Bitcoin, two multi-phase concepts were linked to falling prices: market and sale. The top two words occurring with market were price and US Dollars across each phase of falling prices. The concept sale was discussed in a varying context in different phases of falling prices: with buy[ing] and sell[ing] in phases 2 and 6, bitcoin investors forum 100, token sales in phases 4 and 6 and black friday sales in phase 8 (see Table 6).

Table 6. Top five words occurring with each Bitcoin falling-price, multi-phase concept in phases 2, 4, 6, and 8.

With Ethereum, ten multi-phase concepts were identified. Three of these were associated with rising prices: tax, US Dollars and hit. Hit was discussed with US Dollars (over 40% submissions in each phase of rising prices) and US Dollars were frequently discussed with bitcoin (over 15%), bitcoin investors forum 100. The concept tax was considered with gain (over 30% submissions in bitcoin abc roadmap 32 mb phase of rising prices); pay (over 25%); US Dollars (over 24%) and trade (over 23%). Further details in Table 7.

Table 7. Top five words occurring with each Ethereum rising-price, multi-phase concept in phases 1, 3, and 5.

The remaining seven multi-phase concepts related to falling ether prices. With the exception of game, all these could be split into two themes: price (market bitcoin investors forum 100 bear /bearish /bull ) and innovation (featur ; ceo /cofound ; project /team ; and makerdao /stablecoin ). In each phase of falling prices, bear /bearish /bull was discussed with market (over bitcoin investors forum 100 submissions) and market was discussed with US Dollars (over 20%) and price (over 18%). Price was discussed in the context of bitcoin, which was in over 16% bitcoin investors forum 100 submissions. The context of discussions around innovation varied but referred to new token[s] in over 10% submissions across all concepts and across all phases of falling prices. The concept game was discussed in the context of using gaming machines to mine ether in phase 4 (% submissions) and play[ing] games in phase 6 (% submissions). Further details in Table 8.

Table 8. Top five words occurring with each Ethereum falling-price, multi-phase concept in phases 4 and 6.

The Supplementary Results, in the Supplementary Data Sheet 1 provide further detail on the percentage change in popularity for Bitcoin multi-phase concepts (see Table S8) and Ethereum multi-phase concepts (Table S9).

Coherence With Known Facts

Of the Bitcoin mono-phase themes (see Table 3), regulatory bans are the closest to capturing a specific external event. Discussion of korea and minist /ministri occurred with the debate between the Ministry of Finance and Justice in South Korea as to whether a ban on cryptocurrency trading activity should be implemented, with one proposal being that cryptocurrencies are a scam that should be subject to criminal charges (Jaewon, ). On 16 Decemberwhen prices changed to falling, South Korean news media reported how North Bitcoin investors forum 100 was using hacks of South Korean exchanges to fund its regime, encouraging South Korean support for a ban (Harper, ). This could have triggered South Koreans to sell bitcoin holdings before this became illegal and possibly even criminal (Jaewon, ). Since approximately a fifth of bitcoin transactions were in South Korean Won at the bitcoin investors forum 100 (Jaewon, ), it is coherent with known events that this caused the shift from rising to falling prices. The presence of india in % minist /ministri submissions may reflect concerns over bitcoin regulation, including rumours of a possible ban in India during phase 2 (Lomas, ).

The remaining Bitcoin mono-phase concepts could be reflections of a change in mind-set among bitcoin-holders prior to selling. Before selling, holders of bitcoin are likely to become concerned as to the future of bitcoin (theme Sell or Switch to Altcoin in Table 3) and to consider how to transact the bitcoin held (theme Transaction Practicalities ). Concerned holders of bitcoin may consider: rival cryptocurrencies (cardano /eo /iota /rippl /stellar /tron and airdrop ); Binance, an exchange selling more than cryptocurrencies (Binance, ); and whether to stop reinvesting mining profit[s] from Hashflare (hashflar ) to generate more bitcoin (Ramarao, ). Other bitcoin-holders may dismiss concerns raised on social media platforms (discord ) as price manipulation (pumpanddump ). Before selling bitcoin, holders may consider the practicalities of: reducing fee[s] through batching transactions (batch ) (Harding, bitcoin investors forum 100, bitcoin investors forum 100 seeking support stock investor software exchanges (changelli ); and determining whether transferring bitcoin from a bech32 address is support[ed] (Sedgwick, ).

All the concepts delineated for Ethereum were multi-phase, having a recurring impact on price over time. Innovation (project /team, featur, ceo /cofound and makerdao /stablecoin ) was associated with falling prices (Table 8). This suggests that ether holders disposing of their ether to capitalise on new token[s] from new cryptocurrencies was a cause of price falls. This included project[s] or team[s] develop[ing] (&#x; % submissions) new (&#x; %) token[s] bitcoin investors forum 100 %) through Initial Coin Offerings (ico ; &#x; %). Mentioned in relation to this was ceo /cofound (project &#x; % submissions) and featur (project &#x; % submissions). A separate innovation theme related to interest in MakerDAO, which was launched in December enabling holders to exchange their ether for Dai, a decentralised stablecoin designed to maintain its value in US Dollars (MakerDAO, ).

For Ethereum, price discussed in the context of hit was supported as causing prices to rise whilst market price and sentiment (bear /bearish /bull ) discourse were associated with price falls (see Tables 7, 8). These discussions happened in the context of bitcoin which was a top five co-occurring word throughout. This suggests a source of ether price volatility was traders analysing the ether price and comparing it with bitcoin before buying or selling ether.

The multi-phase concept market was identified as a consistent driver for both falling bitcoin prices and falling ether prices. This was discussed in the context of price as well as buying, trading, and selling (see Tables 6, 8). This supports the widespread influence of technical traders who use just price information to make trading decisions on cryptocurrency price series and is consistent with evidence for price barriers at US Dollars for ether and 6, US Bitcoin investors forum 100 for bitcoin (see Figure 1).

Including contextual analysis in the framework has shown that some multi-phase concepts were polysemic&#x;being used in a different context in different price phases. In some cases, this could be because the concept is an artefact of distinct themes of discussion each happening to include the polysemic concept. For instance, in the case of Ethereum, game was used in the context of using gam[ing] machines to mine ether in phase 4 (mine, card, gpu ) and play[ing] game[s] in phase 6 (see Table 8). Both include the word game but are otherwise distinct issues and so examining the context reveals that game is probably a spurious result.

In contrast, with Bitcoin, the polysemic concept sale became popular in all four phases of falling prices making coincidence less plausible (see Table 6). The concept sale was mentioned in terms of buy[ing] and sell[ing] in phases 2 and 6, bitcoin investors forum 100, a token sale in phases 4 and 6 and black friday sales in phase 8. For sale to be irrelevant to price, distinct, irrelevant themes including sale would have to arise at the correct time across four different phases (falling price phases 2, 4, 6, and 8) and within 24 h before each phase to meet the multi-phase concept criteria. A tenable explanation is that sale is a general term that captures concern regarding bitcoin before decisions to sell. If holders are concerned about bitcoin, they could be more sensitive to any sale of bitcoin (phases 2 and 6); more interested in token sale[s] to exchange bitcoin for other tokens (phases 4 and 6); and more tempted by black friday sale[s] where bitcoins are exchanged for discounted products or sold to generate cash to buy such products (phase 8), bitcoin investors forum 100. This suggests the concept sale may have value as a negative sentiment indicator that warns of future falls in price.

The association of tax with rising ether prices could be explained by the timing of phases 3 and 5, which coincided with the end of tax years when pay[ment] of capit[al] gain[s] tax becomes due (see Table 7). The end of the tax year in some countries, such as the USA (Kagan, ), is on 31 December (phase 3 is from 23 November to 13 January ) but in the UK on 5 April (phase 5 was from 6 April to 5 May ) (Frecknall-Hughes, ).

4. Discussion

Our framework identifies plausible causes of the shifts in ether and bitcoin price trends. Approaches from healthcare epidemiology are deployed that facilitate this move from simply observing how word (Burnie and Yilmaz, b) or topic (Burnie and Yilmaz, a) interest changed across phases in price to identifying the potential causes of these phasic shifts. We find that the framework has to accommodate two distinct types of cause: the multi-phase that repeatedly cause shifts and the mono-phase with a one-off, strong impact. The results for Bitcoin differ from Ethereum, which is consistent with the observed differences in the timing of the highest price and the price phases. We identify a one-off effect of regulatory bans on bitcoin, a repeated effect of rival innovations on ether and the influence of technical traders, captured through market price discourse, on both cryptocurrencies. Traders seem to be comparing the prices of different cryptocurrencies: the Ethereum multi-phase concepts discussed with price commonly referred to bitcoin, and the Bitcoin mono-phase concept covering altcoins (cardano /eo /iota /rippl /stellar /tron ) was discussed with US Dollars.

Previous social media analyses typically required judgement on which metric was most suitable in extracting insights from the social media text. For instance, this pre-selected metric could be a measure of sentiment or be based on a topic modelling algorithm. It was only after the values of the metric had been found that the price data were considered, in testing the association between changes in the metric and price (Kaminski, ; Garcia and Schweitzer, ; Georgoula et al., ; Matta et al., ; Kim et al.,; Abraham et al., ; Steinert and Herff, ).

We move from causal inference, where judgement is required to pre-select which potential causes and what causal mechanism should be tested (Runge et al., ), to causal location, where the best supported causes are located from among social media text. This enables the discovery of new potential causes of price variation which may not have otherwise been considered for testing. None of the potential causes identified (innovation, regulatory bans and technical traders) were suggested by Kim et al. () in a previous analysis of the link between social media topics and bitcoin price. The approach of Kim et al. () required judgement in expanding the list of words within each concept, tested for linear, predictive associations, and did not build a causal argument.

The risk that a concept was spurious was reduced by examining the words within the concept and the words used with that concept, bitcoin investors forum 100, and considering their coherence with known facts (see section ). Concepts containing the word feb or the words christma /holiday /xmas were probably spurious, and could be attributed to the time of year as a confounding factor. The words within the delineated concepts relating to exchanges (binanc /hitbtc and changelli ) did not, in themselves, suggest the influence of a confounding factor. However, these concepts were discussed with send, transact and US Dollar references (see Table 3). Hence, contextual analysis suggests that discussions of exchanges were more plausibly a response to fears over bitcoin price leading to discussion of how best to dispose of bitcoin, rather than a primary cause of falling prices, bitcoin investors forum 100. This contrasts with the concept korea, that was used with ban (Table 3), supporting rumours of a South Korean ban as precipitating the fall from the all time high price.

Multi-phase concepts may have implications for predictive analysis, since these concepts have a predictive association with price that persists across time. Multi-phase concepts may provide an improvement on sentiment metrics such as VADER that have found social media posts to be positive even during falling prices (Abraham et al., ). This extends to polysemic concepts, if their context supports such concepts as acting as proxies for positive or, in the case of sale, negative sentiment. The concept market was supported as a consistent driver of falling prices for both bitcoin and ether. However, the other multi-phase concepts bitcoin investors forum 100, suggesting that different predictors may be suitable for different cryptocurrencies. Predictive modelling faces the limitation of one-off, impactful mono-phase events shaping the price trend. These may be considered analogous to black swan (Taleb, ) events, being unexpected and having a major impact, but they can be rationalised with the benefit of hindsight.

Future work could examine whether black swan events can be found in cryptocurrencies other than Bitcoin and whether such events bitcoin investors forum 100 shared or unique to a specific cryptocurrency. Better understanding of the causes of shifts between price phases will help investors in diversifying their cryptocurrency investments to reduce risk.

Data Availability Statement

The sources of data are listed in section The code used to prepare and analyse this data is publicly accessible in a Dryad data repository (Burnie et al., ) at: www.oldyorkcellars.com

Author Contributions

AB processed and analysed the data, and drafted the article. EY and TA provided critical feedback on the article, inputting on the data processing and analysis approaches taken. All authors gave final approval for publication and agree to be held accountable for the work performed therein.

Funding

This work was supported by The Alan Turing Institute under the EPSRC grant EP/N/1 and Turing award number TU/C/ This project was partially funded by the EPSRC Fellowship titled Task Based How to make fake money that looks real Retrieval (grant reference number EP/P/1); BARAC project (EP/P/1); and FinTech project (HICT ).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships how make money on stocks could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: www.oldyorkcellars.com#supplementary-material

References

Abstract

Citation: Kim YB, Lee J, Park N, Choo J, Kim J-H, Kim Bitcoin investors forum 100 () When Bitcoin encounters information in an online forum: Using text mining to analyse user opinions and predict value fluctuation. PLoS ONE 12(5): e www.oldyorkcellars.com

Editor: Kim-Kwang Raymond Choo, University of Texas at San Antonio, UNITED STATES

Received: January 16, ; Accepted: May 1, ; Published: May 12,

Copyright: © 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 bitcoin investors forum 100 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(NRFM3C1B, NRFM3C1B, NRFR1A2B) and Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIP)(R,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”, bitcoin investors forum 100, has changed the way economic transactions are conducted among Internet users to a great extent. 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.

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], bitcoin investors forum 100, 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 bitcoin investors forum 100 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 bitcoin investors forum 100 an instant overview of the corpus, thereby obviating the need to read through comments, which would otherwise be a tedious, bitcoin investors forum 100, time-consuming process.

Recently, bitcoin investors forum 100, 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].

Methods

Prediction modelling

Granger causality test.

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]. Nevertheless, connection does not imply causation. We test whether the time arrangement of a discussion of conclusions contains any prescient data with respect to vacillations in the Bitcoin transaction and price.

Our time arrangement at the Bitcoin transaction count and price, indicated by S_t, reflects day-to-day change in the Bitcoin transaction count and price. To test whether the idea of gathering feelings in the time arrangement could forecast the change in the vacillation in terms of the Bitcoin transaction and price, we considered the difference clarified by two linear models as in (5) and (6) below. The first model uses just n slacked estimations of S_t for the forecast. However, the second model uses the n slacked estimations of both S_t and the time series of a concept of forum opinions, meant by X_t−1,⋯,X_t−n. We completed the Granger causality test as indicated by the models in (5) and (6).

(5)(6)

In view of the consequences of the Granger causality test, we can reject the null hypothesis, whereby the time series of a concept of forum opinions does not predict fluctuations in the Bitcoin transaction count and price with a high level of confidence. The Granger causality test was performed on the Bitcoin transaction count and price for a time lag of 1 to 12 days.

Deep learning model.

Using the gathered data and the analysed and rated comment data, we built a model for predicting the fluctuation in the Bitcoin price and transaction through deep learning. Deep learning is widely used for addressing diverse challenges[8, 39]. Despite the quantitative and qualitative increases in Bitcoin-related formal and informal data following the broadening applicability of Bitcoin, bitcoin investors forum 100, deep learning has rarely been used to explore Bitcoin price trends and to address other Bitcoin-related challenges. We created a setting to apply deep learning to the data spanning a period of years.

As the first step, we standardised the data to improve its applicability to the learning model. An example of applicable input data is provided in Table 2.

Download:

Table 2. Example of deep learning data set.

The z-score (, where and represent the mean and standard deviation for every date, respectively) of data for the previous 12 days (t = 12) was used as the values.

www.oldyorkcellars.com

Subsequently, to use the input data for prediction, we set up a deep learning model. Multiple hidden layers were accumulated for learning to identify deep data structures. Specifically, bitcoin investors forum 100, 1, 2, 3, and 5 hidden layers were constructed to select the layer structure that returned the best possible prediction result. The number of neurons that were allocated to each hidden layer was 1,

As for the input layers, based on the input data provided in Table 2, 15 input data points were represented as serial vectors to allocate neurons based on the cumulative number of days spent on learning, i.e. 45, bitcoin investors forum 100,bitcoin investors forum 100, and neurons were allocated to cumulative 3, 5, 7, and 12 days. As for the output layer, two neurons were allocated while the probability of rise/fall was represented with the softmax function. The prediction model was built using Google Tensorflow[7], and GPU operation (nVIDIA CUDA) was used to accelerate the deep learning process.

Results

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, bitcoin investors forum 100, 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 or less, which was significant. Bitcoin investors forum 100 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 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 bitcoin investors forum 100 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, bitcoin investors forum 100 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, bitcoin investors forum 100. The proposed method for predicting the fluctuation in the Bitcoin price and transactions based on user bitcoin investors forum 100 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

1. Conceptualization: YBK CHK.
2. Data curation: JL JC NP.
3. Formal analysis: YBK JL JC CHK.
4. Investigation: YBK JL JC.
5. Methodology: YBK JL JC CHK.
6. Project administration: YBK JC CHK.
7. Software: JL NP JC.
8. Supervision: JC CHK.
9. Validation: YBK NP.
10. Visualization: YBK JL JHK.
11. Writing – original draft: YBK JL NP JC CHK.
12. Writing – review & editing: YBK JL JC JHK.

References

1. 1. Nakamoto S. Bitcoin: A peer-to-peer electronic cash system.
2. 2. Böhme R, Christin N, Edelman B, Moore T. Bitcoin: Economics, technology, and governance. The Journal of Economic Perspectives. ;29(2)–
3. 3. Grinberg R. Bitcoin: An innovative alternative digital currency. Hastings Sci & Tech LJ. ;
4. 4. Barber S, Boyen X, Shi E, Uzun Easy way to earn money online. Bitter to better—how to make bitcoin a better currency. International Conference on Financial Cryptography and Data Security. –
5. 5. Reid F, Harrigan M. An analysis of anonymity in the bitcoin system. Security and privacy in social networks. –
6. 6. Kim YB, Kim JG, Kim W, Im JH, Kim TH, bitcoin investors forum 100, Kang SJ, et al. Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies. PLOS ONE. ;11(8):e pmid
7. 7. Abadi M, Agarwal A, bitcoin investors forum 100, Barham P, Brevdo E, Chen Z, Citro C, et al. Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv
8. 8. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. ;()– pmid
9. 9. Matta M, Lunesu I, Marchesi M, editors. Bitcoin Spread Prediction Using Social and Web Search Media. UMAP Workshops;
10. Kaminski J. Nowcasting the Bitcoin Market with Twitter Signals. arXiv preprint arXiv
11. Kristoufek L. BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era. Scientific reports. ;3.
12. Kristoufek L. What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analysis. PloS one. ;10(4):e pmid
13. Yelowitz A, Wilson M. Characteristics of Bitcoin users: an analysis of Google search data. Applied Economics Letters. ;22(13)–6, bitcoin investors forum 100.
14. Athey S, Parashkevov I, Sarukkai V, Xia J. Bitcoin pricing, adoption, and usage: Theory and evidence.
15. Bernstein MS, Monroy-Hernández A, Harry D, André P, Panovich K, Vargas GG. 4chan and/b: An Analysis of Anonymity and Ephemerality in a Large Online Community. ICWSM. –7.
16. Hau YS, Kim Y-G. Why would online gamers share their innovation-conducive knowledge in the online game user community? Integrating individual motivations and social capital perspectives. Computers in Human Behavior. ;27(2)–
17. Kim YB, Lee SH, Kang SJ, Choi MJ, Lee J, Kim CH. Virtual world currency value fluctuation prediction system based on user sentiment analysis. PloS one. ;10(8):e pmid
18. Panzarasa P, Opsahl T, Carley KM. Patterns and dynamics of users' behavior and interaction: Network analysis of an online community. Journal of the American Society for Information Science and Technology. ;60(5)–
19. Sing CC, Khine MS. An analysis of interaction and participation patterns in online community. JOURNAL OF EDUCATIONAL TECHNOLOGYAND SOCIETY. ;9(1)
20. Fleder M, Kester MS, Pillai S. Bitcoin transaction graph analysis. arXiv preprint arXiv
21. Cohen-Charash Y, Scherbaum CA, Kammeyer-Mueller JD, Staw BM. Mood and the market: can press reports of investors' mood predict stock prices? PloS one. ;8(8):e pmid
22. Bollen J, Mao H, Zeng X. Twitter mood predicts the stock market. Journal of Computational Science. ;2(1):1–8.
23. Linton M, Teo EG, Bommes E, Chen CY-H, Härdle WK. Dynamic Topic Modelling for Cryptocurrency Community Forums, bitcoin investors forum 100.
24. Blei DM, Ng AY, Jordan MI. Latent dirichlet allocation. Journal of machine Learning research. ;3(Jan)–
25. Lee DD, Seung HS. Learning the parts of objects by non-negative matrix factorization. Nature. ;()– pmid
26. Wang C, Blei DM. Collaborative topic modeling for recommending scientific articles. Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. –
27. Wang Y, Agichtein E, Benzi M. TM-LDA: efficient online modeling of latent topic transitions in social media. Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. –
28. Xianghua F, Guo L, Yanyan G, Zhiqiang W. Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon. Knowledge-Based Systems. ;–
29. Bohr J, Bashir M. Who uses bitcoin? an exploration of the bitcoin community. Privacy, bitcoin investors forum 100, Security and Trust (PST), Twelfth Annual International Conference on. –
30. Choi H, Varian H. Predicting the present with Google Trends. Economic Record. ;88(s1):2–9.
31. Preis T, Moat HS, Stanley HE. Quantifying trading behavior in financial markets using Google Trends. Scientific reports. ;3.
32. Kristoufek L. Can Google Trends search queries contribute to risk diversification? Scientific reports. ;3.
33. Kang M, Zhong H, He J, Rutherford S, Yang F. Using google trends for influenza surveillance in South China. PloS one. ;8(1):e pmid
34. Kim YB, Park N, bitcoin investors forum 100, Zhang Q, Kim JG, Kang SJ, Kim CH. Predicting Virtual World User Population Fluctuations with Deep Learning. PLOS ONE. ;11(12):e pmid
35. Moat HS, Curme C, Avakian A, Kenett DY, Stanley HE, Preis T. Quantifying Wikipedia usage patterns before stock market moves. Scientific reports. ;3.
36. Mestyán M, Yasseri T, Kertész J. Early prediction of movie box office success based on Wikipedia activity big data. PloS one. ;8(8):e pmid
37. Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. arXiv preprint arXiv
38. Granger CW, Huangb B-N, Yang C-W. A bivariate causality between stock prices and exchange rates: evidence from recent Asianflu☆. The Bitcoin investors forum 100 Review of Economics and Finance. ;40(3)–
39. Schmidhuber J. Deep learning in neural networks: An overview. Neural Networks. ;– pmid
40. Matta M, Lunesu I, bitcoin investors forum 100, Marchesi M. Bitcoin spread prediction using social and web search media. Proceedings of DeCAT.
41. Juea W, Bitcoin investors forum 100 Z, Bao-huab Z, Cheng-ronga W. Invest green stocks Forum Opinion Leaders Discovering Method Based on Clustering Analysis [J]. Computer Engineering. ;
42. Li N, Wu DD. Using text mining and sentiment analysis for online forums hotspot detection and forecast. Decision support systems. ;48(2)–
43. Coles BA, West M. Trolling the trolls: Online forum users constructions of the nature and properties of trolling. Computers in Human Behavior. ;–
44. Benevenuto F, Rodrigues T, Cha M, Bitcoin investors forum 100 V. Characterizing user behavior in online social networks. Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference. –
45. Maia M, Almeida J, Almeida V. Identifying user behavior in online social networks. Proceedings of the 1st workshop on Social network systems. –6.
46. Rout J, Dalmia A, Choo K-KR, Bakshi S, Jena S. Revisiting Semi-supervised Learning for Online Deceptive Review Detection. IEEE Access.
47. Feng S, Banerjee R, Choi Y, editors. Syntactic stylometry for deception detection. Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers-Volume 2; Association for Computational Linguistics.
48. Shin Y, Gupta M, Myers S, editors. Prevalence and mitigation of forum spamming. INFOCOM, Proceedings IEEE; IEEE.
49. Shin Y, Gupta M, Myers SA, editors. The Nuts and Bolts of a Forum Spam Automator. LEET;
50. Hayati P, Potdar V, editors. Bitcoin investors forum 100 spam an evaluation of web anti-spam methods. Industrial Bitcoin investors forum 100, INDIN 7th IEEE Bitcoin investors forum 100 Conference on; IEEE.
51. Choo K-KR. Designated non-financial businesses and professionals: A review and analysis of recent financial action task force on money laundering mutual evaluation reports. Security Journal. ;27(1):1–
52. Choo K-KR. Cryptocurrency and Virtual Currency: Corruption and Money Laundering/Terrorism Financing Risks? Handbook of Digital Currency: Bitcoin investopedia analysis, Innovation, Financial Instruments, and Big Data.
53. Bryans D. Bitcoin and money laundering: mining for an effective solution. Ind LJ. ;
54. Stokes Bitcoin investors forum 100. Virtual money laundering: the case of Bitcoin and the Linden dollar. Information & Communications Technology Law. ;21(3)–