Social Media Perceptions of 51% Attacks on Proof-of-Work Cryptocurrencies: A Natural Language Processing Approach

This work is the first study on the effects of 51% attacks on proof-of-work (PoW) cryptocurrencies as expressed in the sentiments and emotions of social media users. Our goals are to design the methodologies for the study including data collection, conduct volumetric and temporal analyses of the data, and profile the sentiments and emotions that emerge from the data. As a first step, we have identified 31 events of 51% attacks on various PoW cryptocurrencies. We have designed the methodologies and gathered Twitter data on the events as well as benchmark data during normal times for comparison. We have defined parameters for profiling the datasets based on their sentiments and emotions. We have studied the variation of these sentiment and emotion profiles when a cryptocurrency is under attack and the benchmark otherwise, between multiple attack events of the same cryptocurrency, and between different cryptocurrencies. Our results confirm some expected overall behaviour and reactions while providing nuanced insights that may not be obvious or may even be considered surprising. Our code and datasets are publicly accessible

Public Perceptions of Facebook’s Libra Digital Currency Initiative: Text Mining on Twitter

Large corporations in the financial and technology sectors are increasingly interested in digital currencies, and central bank digital currencies are being actively researched around the globe. In this study, we analyzed the public discourse conducted through the social media platform Twitter concerning Facebook’s Libra digital currency initiative. Text mining of tweets posted during the one-month period around the official announcement of the digital currency project revealed that the majority of the public have a neutral sentiment toward the proposed digital currency. However, those with positive attitudes outnumbered those perceiving the digital currency initiative as negative, and the negative sentiment mainly stemmed from anger and anxiety. Through topical modeling analysis using latent Dirichlet allocation, we identified eight themes in the public discourse related to Facebook Libra. The study provides an early exploratory assessment of factors facilitating and hindering user adoption of one of the most important practical applications of blockchain technology

Blockchain Value Creation Logics and Financial Returns

With its complexities and portfolio-nature, the advent of blockchain technology presents several use cases to stakeholders for business value appropriation and financial gains. This 3-essay dissertation focuses on three exemplars and research approaches to understanding the value creation logics of blockchain technology for financial gains. The first essay is a conceptual piece that explores five main affordances of blockchain technology and how these can be actualized and assimilated for business value. Based on the analysis of literature findings, an Affordance-Experimentation-Actualization-Assimilation (AEAA) model is proposed. The model suggests five affordance-to-assimilation value chains and eight value interdependencies that firms can leverage to optimize their value creation and capture during blockchain technology implementation. The second essay empirically examines the financial returns of public firms\u27 blockchain adoption investments at the level of the three main blockchain archetypes (private-permissioned, public-permissioned and permissionless. Drawing upon Fichman\u27s model of the option value of innovative IT platform investments, the study examines business value creation through firm blockchain strategy (i.e., archetype instances, decentralization, and complementarity), learning (i.e., blockchain patents and event participation), and bandwagon effects using quarterly data of firm archetype investments from 2015 to 2020. The study\u27s propensity score matching utilization and fixed-effects modeling provide objective quantification of how blockchain adoption leads to increases in firm value (performance measured by Tobin\u27s q) at the archetype level (permissionless, public permissioned, and private permissioned). Surprisingly, a more decentralized archetype and a second different archetype implementation are associated with a lower Tobin\u27s q. In addition, IT-option proxy parameters such as blockchain patent originality, participation in blockchain events, and network externality positively impact firm performance, whereas the effect of blockchain patents is negative. As the foremost and more established use case of blockchain technology whose business value is accessed in either of the five affordances and exemplifies a permissionless archetype for financial gains, bitcoin cryptocurrency behavior is studied through the lens of opinion leaders on Twitter. The third essay this relationship understands the hourly price returns and volatility shocks that sentiments from opinion leaders generate and vice-versa. With a dynamic opinion leader identification strategy, lexicon and rule-based sentiment analytics, I extract sentiments of the top ten per cent bitcoin opinion leaders\u27 tweets. Controlling for various economic indices and contextual factors, the study estimates a vector autoregression model (VAR) and finds that finds that Bitcoin return granger cause Polarity but the influence of sentiment subjectivity is marginal and only stronger on bitcoin price volatility. Several key implications for blockchain practitioners and financial stakeholders and suggestions for future research are discussed

Kako su aktivnosti na Twitteru povezane s ponašanjem najpoznatijih kriptovaluta? Dokazi iz analize društvenih mreža i analize sentimenta

Cryptocurrencies have embraced Twitter as a major channel of communication. Employing social network analysis and sentiment analysis, this study investigates the Twitter-mediated communication behaviors among cryptocurrencies. This study determines whether a significant association exists between cryptocurrencies\u27 Twitter networks and their credit scores. Data were drawn from the Twitter pages of several top cryptocurrencies. The results indicate that reply–mention networks had the densest structure, that the following–follower network structure was correlated with the reply–mention structure, and that the reply–mention and co–tweet networks were positively correlated. The results also indicate that cryptocurrencies\u27 active networking strategies affected their credit scores and more importantly, that cryptocurrencies frequently linked with fellow currencies tended to have high credit scores.Kriptovalute su prigrlile Twitter kao glavni kanal komunikacije kojim prenose novosti i grade odnose s (potencijalnim) ulagačima i kupcima. Služeći se analizom društvenih mreža i analizom sentimenta, rad istražuje Twitterom posredovano komunikacijsko ponašanje kriptovaluta proučavanjem učestalosti tvitova te njihovih struktura: following-follower, reply-mention i co-tweet. Ocjene tržišta često znatno utječu i na proizvođače (tj. programere) i na potrošače (tj. vlasnike kriptovaluta). Stoga ovo istraživanje utvrđuje postoji li povezanost između Twitterovih mreža kriptovaluta i njihovih kreditnih ocjena. Podaci su prikupljeni na Twitterovim stranicama niza najpoznatijih kriptovaluta. Rezultati pokazuju da su reply-mention mreže imale najgušću strukturu, da je mrežna struktura following-follower povezana sa strukturom reply-mention i da su reply-mention i co-tweet mreže pozitivno povezane. Rezultati također upućuju na to da su aktivne mrežne strategije kriptovaluta utjecale na njihove kreditne ocjene i, što je još važnije, da kriptovalute koje se češće povezuju sa srodnim valutama obično imaju visoke kreditne ocjene

Influencers, are they responsible for Bitcoin's volatility? Transfer entropy and Granger causality in prol of an answer

Bitcoin, like any other cryptocurrency, is subject to fluctuations in price. The volatility of this market can be a reflection of several reasons, such as public opinion, social networks and news. Social networks, in particular Twitter, are increasingly used as an important source of value extraction because through this network, it is possible to find out about news in real-time, follow the repercussions, know what experts in the financial world are commenting or thinking and even decide based on influencer's opinion whether to invest or not. This study investigates the influence that a specific group of people exert on Bitcoin volatility. A selection of influencers from the “crypto world” was made, and through the Twitter API, it was possible to select the tweets of the object of study. To choose the classification model for sentiment analysis, two techniques were compared, one being very popular with a focus on the domain of social networks and the other recently created and focused on finance. From the selected technique, only positive and negative sentiments were considered, and then the daily series of the Sentiment Score was calculated. Next, the causal relationship between Bitcoin and sentiment was investigated using Granger causality and Transfer Entropy tests. Transfer Entropy showed encouraging results, suggesting that there is a transfer of information from Sentiment to Returns and that it is possible for an influencer to contribute to Bitcoin’s volatilityO Bitcoin, assim como qualquer outra criptomoeda, está sujeito a flutuações no preço. A volatilidade desse mercado pode ser reflexo de vários motivos, tais como, opinião pública, redes sociais e notícias. As redes sociais, em particular o Twitter, cada vez mais é utilizado como uma fonte importante de extração de valor, isto porque através desta rede é possível saber das novidades em tempo real, acompanhar as repercussões, saber o que entendedores do mundo financeiro estão a comentar e decidir até mesmo com base na opinião de um influenciador se irá investir ou não. Este estudo investiga a influência que determinadas pessoas exercem sobre a volatilidade do Bitcoin. Foi feita uma seleção de influenciadores do “mundo crypto” e através da API do Twitter foi possível selecionar os tweets de objeto de estudo. Para a escolha do modelo de classificação para análise de sentimento foram comparadas duas técnicas, sendo uma muito popular com foco no domínio de redes sociais e a outra recém-criada e focada em finanças. A partir da técnica selecionada, apenas os sentimentos positivos e negativos foram considerados e então calculada a série diária do Sentiment Score. A seguir foi investigada a relação causal entre o Bitcoin e o sentiment utilizando os testes de causalidade de Granger e Entropia de Transferência. A Entropia de Transferência mostrou resultados animadores que sugerem existir transferência de informação de Sentiment para Returns e que, portanto, é possível que um influencer contribua para a volatilidade do Bitcoin

Understanding the Relationship between Online Discussions and Bitcoin Return and Volume: Topic Modeling and Sentiment Analysis

This thesis examines Bitcoin related discussions on Bitcointalk.com over the 2013-2022 period. Using Latent Dirichlet Allocation (LDA) topic modeling algorithm, we discover eight distinct topics: Mining, Regulation, Investment/trading, Public perception, Bitcoin’s nature, Wallet, Payment, and Other. Importantly, we find differences in relations between different topics’ sentiment, disagreement (proxy for uncertainty) and hype (proxy for attention) on one hand and Bitcoin return and trading volume on the other hand. Specifically, among all topics, only the sentiment and disagreement of Investment/trading topic have significant contemporaneous relation with Bitcoin return. In addition, sentiment and disagreement of several topics, such as Mining and Wallet, show significant relationships with Bitcoin return only on the tails of the return distribution (bullish and bearish markets). In contrast, sentiment, disagreement, and hype of each topic show significant relation with Bitcoin volume across the entire distribution. In addition, whereas hype has a positive relation with trading volume in a low-volume market, this relation becomes negative in a high-volume market

HOW DO LARGE STAKES INFLUENCE BITCOIN PERFORMANCE? EVIDENCE FROM THE MT.GOX LIQUIDATION CASE

Bitcoin as the first and still most important decentralized cryptocurrency has gained wide popu-larity due to the steep rise of its price during the second half of 2017. Because of its digital na-ture, Bitcoin cannot be valuated exclusively with fundamental approaches, which is why factors such as investor sentiment have become a common alternative to capture its performance. In this work, we studied whether and how the sale of Bitcoins from the insolvency assets of Mt.Gox, which represent about 1.1% of the current global total, relates to Bitcoin price movements. We used social media sentiment analysis of Twitter data to examine how investors are influenced in their decision to buy or sell Bitcoin when confronted with the trade actions of Nobuaki Koba-yashi, the trustee in charge of the Mt.Gox case. We built a vector error correction model to ana-lyze the long-run relationship between cointegrated variables. Our analysis confirms the posi-tive association of Bitcoin performance with positive Twitter sentiment and tweet volume and the negative association with negative sentiment. We further found empirical evidence that Mt.Gox selloff events have a lasting negative impact on the Bitcoin price and that we can measure this effect by Twitter sentiment and tweet volume

Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this problem-solving metric perspective, i.e., as technical analysis, forecasting, and estimation using a standardized ledger-based technology. The envisioned tools based on forecasting are then suggested, i.e., ranking Initial Coin Offering (ICO) values for incoming cryptocurrencies, trading strategies employing the new Sentiment Analysis metrics, and risk aversion in cryptocurrencies trading through a multi-objective portfolio selection. Our perspective is rationalized on the perspective on elastic demand of computational resources for cloud infrastructures

Ramalan harga Bitcoin berasaskan polariti sentiment artikel berita dan data pasaran dengan model LSTM

Bitcoin adalah wang digital dan alat pelaburan yang telah mendapat perhatian seluruh dunia sejak kebelakangan ini. Namun, harga Bitcoin yang tidak stabil telah menjadi kebimbangan di kalangan pengguna dan pelabur Bitcoin. Ramalan harga Bitcoin dapat membantu pelabur dan pengguna untuk membina strategi yang efektif dalam pelaburan atau penggunaan. Dengan perkembangan pesat Internet, data dalam talian termasuk artikel berita boleh membantu dalam harga ramalan Bitcoin. Kajian ini bertujuan untuk mengkaji kesan sentimen artikel berita kepada harga Bitcoin dengan tempoh kajian dari September 2017 hingga Ogos 2019. Sehubungan dengan itu, kajian ini memperkenalkan analisis sentimen untuk memahami maklumat artikel berita dalam talian dan menggunakannya sebagai fitur input untuk ramalan harga Bitcoin. Terdapat dua fasa utama dalam kajian ini, iaitu analisis sentimen dan ramalan harga. Dalam analisis sentimen, sentimen diekstrak berdasarkan kaedah leksikon untuk memahami maklumat artikel berita berkaitan dengan pasaran kriptowang. Kriptowang adalah sejenis sistem pembayaran digital dan monetari yang mana transaksi dilakukan dengan cara desentralisasi yang merupakan transaksi kewangan rakan-ke-rakan tanpa melalui institusi kewangan. Dengan kata lain, Bitcoin tidak bergantung kepada perantara pihak ketiga untuk memproses pembayaran, ia menggunakan bukti kriptografi dalam komputer untuk memproses dan mengesahkan kesahihan dan menyebarkan antara rangkaian (Nakamoto 2008). Dalam ramalan harga, sentimen digunakan sebagai fitur input dan model Memori Jangka Panjang Pendek (LSTM) digunakan dalam fasa ramalan harga. Dengan data pasaran dan artikel berita sebagai sampel, keputusan menunjukkan sentimen artikel berita dapat mengurangkan kesilapan dalam ramalan harga Bitcoin