Leveraging Twitter data to understand the dynamics of social media interactions on cryptocurrencies

Rapid technological change in the last decades has led to the emergence of new platforms and fields such as cryptocurrencies and social media data. Cryptocurrencies are decentralized digital currencies that use blockchain technology to create a secure and decentralized environment. In the decade since the inception of social media, it has created revolutions and connected people with interests. Social media platforms such as Twitter allow users worldwide to share opinions, emotions, and news. Twitter is one of the most used social media platforms worldwide. The social media platform has millions of users where tweets are continuously shared every second. Therefore, tweets are useful when a large amount of data is generated to conduct a social media analysis. In addition, Twitter is broadly utilized by investors and financial analysts to gather valuable information. Several studies have shown that the content posted on Twitter can predict the movement of cryptocurrency prices. However, limited research has been conducted on the dynamics of Twitter interactions on cryptocurrencies among users. By leveraging 1724328 tweets, this research aims to understand the dynamics of social media users’ interactions on cryptocurrencies. Essentially by shedding light on larger cryptocurrencies contrary to smaller. The findings reveal that Twitter users are more positive than negative about cryptocurrencies. The analysis also shows an existing relationship between events and the interaction of users, where cryptocurrency-related events shift the emotion, sentiment, and discussion topics of the users. The thesis contributes to demonstrating the effectiveness of the Social set analysis framework to analyze and visualize a massive amount of social media data and user-generated data created on social media platforms such as Twitter

Сryptocurrency and Internet of Things: Problems of Implementation and Realization

IoT (Internet of Things) requires the implementation of digital encryption of information, transaction support and recording of all events for security. It can provide cryptocurrencies protocols with adding an additional possibility of payments. This opportunity is not so much demanded at the hardware level as in the software implementation. We have discovered that IoT devices are widely used for illegal purposes for trusts or network consolidated attacks, and virtually no legal and useful ways of using their hardware-distributed capabilities. Standardization and compatibility in IOT network should become the main tools for the possibility of introducing new solutions, testing their utility, performance and safety. The standardization of a new approach to interactive protocols in the IOT network and the Internet with a finance approach is now inevitable. We need new IEEE standards for cryptocurrencies and IoT functioning. They must include standards for protocol functioning, transaction validation and saving, privacy and security support. Cryptocurrencies and IoT interaction diagram were proposed. The IoT network devices technology will be in the future instance of the smart class of digital-physical systems, which also encompasses technologies such as smart homes, intelligent transportation systems, smart cities etc. The financial aspect for purchasing software, services, solutions and sales of the resulting benefits will complement this network with additional capabilities. The development of standards for the financial level of functioning is also necessary.IoT (Internet of Things) requires the implementation of digital encryption of information, transaction support and recording of all events for security. It can provide cryptocurrencies protocols with adding an additional possibility of payments. This opportunity is not so much demanded at the hardware level as in the software implementation. We have discovered that IoT devices are widely used for illegal purposes for trusts or network consolidated attacks, and virtually no legal and useful ways of using their hardware-distributed capabilities. Standardization and compatibility in IOT network should become the main tools for the possibility of introducing new solutions, testing their utility, performance and safety. The standardization of a new approach to interactive protocols in the IOT network and the Internet with a finance approach is now inevitable. We need new IEEE standards for cryptocurrencies and IoT functioning. They must include standards for protocol functioning, transaction validation and saving, privacy and security support. Cryptocurrencies and IoT interaction diagram were proposed. The IoT network devices technology will be in the future instance of the smart class of digital-physical systems, which also encompasses technologies such as smart homes, intelligent transportation systems, smart cities etc. The financial aspect for purchasing software, services, solutions and sales of the resulting benefits will complement this network with additional capabilities. The development of standards for the financial level of functioning is also necessary

Teaching Case: The Initial Coin Offering Marketplace: A Data Analytic Case

This case uses data analytic techniques to expose students to the context of the initial coin offering marketplace. The exercise is well-suited as a group activity in an undergraduate or graduate business analytics course in which students have been taught analytic techniques such as word cloud, descriptive statistics, basic visualizations, and decision tree analysis. The data comes from the real-world initial coin offering market, so students learn about the business aspects of the initial coin offering market and the underlying technology of blockchain. As a result, the students gain a chance to practice basic analytic techniques and leverage those techniques to learn more about the business context. This project-based case provides scripts for instructors using the free, open-source software R; however, an instructor may choose alternative analytic platforms to implement the analysis. The activity may also be made accessible for novice analytics students as a first experience in R by providing the accompanying solution scripts to the students

Predicting the Price of Cryptocurrency Using Machine Learning Algorithm

It is proposed to conduct a project aimed at forecasting cryptocurrency price values. The concept of cryptocurrencies refers to computerized money that is used for a variety of transactions as well as for long-term investments. The most common cryptocurrency that most of the systems use to conduct their transactions is the Ethereum cryptocurrency. However, it needs to be noted that there are many other well-known crypto currencies other than ethereum as well. We propose to use Machine Learning for this project, which will be trained from the available cryptocurrency price data, to gain intelligence, and then use this knowledge to make accurate predictions. Trading cryptocurrency prices is one of the most popular exchanges right now. It is suggested that both day traders and investors can benefit greatly from using the suggested approach

Data as capital and ethical implications in digital sport business models

Professional sport has entered the digital economy as organisations adopt data-driven business model innovations. The purpose of this article is to highlight the potential ethical vulnerabilities sport organisations and their leaders face when adopting digital sport business models. Here, we treat data as a species of capital that can be converted into economic capital once it undergoes a computational transformation via a data-driven business model innovation. We argue for two advantages in this approach. First, it helps make transparent the mechanisms through which digital sport business models work. Second, it reveals how the extraction and application of big data exacerbates inequitable power relationships between sport organisations and supporters – the big data divide – that leads to ethical vulnerabilities for sport organisations and their consumers. We suggest that sport consumers might be particularly vulnerable to digital data risk as a consequence of their high levels of brand loyalty and involvement, which tend to encourage trust in the sport properties soliciting, analysing, and monetising their data. Platform broadcasting partnerships, e-ticketing in smart stadiums, and cryptocurrency-based fan tokens are used as examples of data-driven business model innovations based on the conversion of data to capital, demonstrating how sport organisations risk violating the trust of supporters when using digital strategies. The article concludes with directions for future research to deliver an ethically informed data-driven sports industry.</p

Leveraging Explainable AI to Support Cryptocurrency Investors

In the last decade, cryptocurrency trading has attracted the attention of private and professional traders and investors. To forecast the financial markets, algorithmic trading systems based on Artificial Intelligence (AI) models are becoming more and more established. However, they suffer from the lack of transparency, thus hindering domain experts from directly monitoring the fundamentals behind market movements. This is particularly critical for cryptocurrency investors, because the study of the main factors influencing cryptocurrency prices, including the characteristics of the blockchain infrastructure, is crucial for driving experts’ decisions. This paper proposes a new visual analytics tool to support domain experts in the explanation of AI-based cryptocurrency trading systems. To describe the rationale behind AI models, it exploits an established method, namely SHapley Additive exPlanations, which allows experts to identify the most discriminating features and provides them with an interactive and easy-to-use graphical interface. The simulations carried out on 21 cryptocurrencies over a 8-year period demonstrate the usability of the proposed tool

Does Social Media Sentiment Predict Bitcoin Trading Volume?

Social media sentiment is proven to be an important feature in financial forecasting. While the effect of sentiment is complex and time-varying for traditional financial assets, its role in cryptocurrency markets is unclear. This research explores the predictive power of public sentiment on Bitcoin trading volume. We develop a novel sentiment analysis pipeline for processing Bitcoin-related tweets and achieve state-of-the-art accuracy on a benchmark dataset. Our pipeline also leverages information gain theory to incorporate the impact of textual and non-textual features. We use such features to discern a non-linear relationship between public sentiment and Bitcoin trading volume and discover the optimal predictive horizon for Bitcoin. This research provides a useful module and a foundation for future studies and understanding of Bitcoin market dynamics, and its interaction with social media buzzing

Tracing Transactions Across Cryptocurrency Ledgers

One of the defining features of a cryptocurrency is that its ledger, containing all transactions that have evertaken place, is globally visible. As one consequenceof this degree of transparency, a long line of recent re-search has demonstrated that even in cryptocurrenciesthat are specifically designed to improve anonymity it is often possible to track money as it changes hands,and in some cases to de-anonymize users entirely. With the recent proliferation of alternative cryptocurrencies, however, it becomes relevant to ask not only whether ornot money can be traced as it moves within the ledgerof a single cryptocurrency, but if it can in fact be tracedas it moves across ledgers. This is especially pertinent given the rise in popularity of automated trading platforms such as ShapeShift, which make it effortless to carry out such cross-currency trades. In this paper, weuse data scraped from ShapeShift over a thirteen-monthperiod and the data from eight different blockchains to explore this question. Beyond developing new heuristics and creating new types of links across cryptocurrency ledgers, we also identify various patterns of cross-currency trades and of the general usage of these platforms, with the ultimate goal of understanding whetherthey serve a criminal or a profit-driven agenda.Comment: 14 pages, 13 tables, 6 figure

Marketing Strategies in Web3: Exploring Transformation of Decentralized Landscape

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Digital Marketing and AnalyticsThis thesis explores the data-driven approaches that marketers can implement to engage and retain communities in the Web3 landscape. With the emergence of decentralized technologies and blockchain-based platforms, marketing in the digital landscape has undergone a significant transformation. However, there is a lack of practical and research-backed insights on Web3 marketing strategies. The objectives of this research are to compare and contrast the marketing strategies used in Web2 and Web3, explore the type and use of data in Web3 marketing, gather insights into the current and future state of Web3 marketing, and provide practical recommendations for marketers and businesses. The research methodology includes a mixed methods approach, combining a literature review and in-depth interviews with industry experts. Through the analysis of primary and secondary data, several key findings emerge. Firstly, Web3 marketing is community-centric, emphasizing the importance of long-term community relationships over short-term sales. Secondly, data plays a crucial role in Web3 marketing, with a shift towards leveraging on-chain data and publicly available blockchain insights. Thirdly, community engagement, retention, and loyalty are essential strategies in Web3 marketing. Nurturing communities, fostering regular interaction, and incorporating community insights into decision-making processes are key to success. Tokenization and incentivization strategies, such as airdrops and gamification, play a significant role in engagement, while continuous innovation and strategic planning are critical for retention. Overall, this research provides valuable insights and recommendations for marketers and businesses in the rapidly evolving Web3 marketing landscape