GALACTICOIN: A new revenue stream for Real Madrid based on blockchain technology

White paper.SUMMARY: Football is indeed a beautiful game, and its appeal is unrivaled. This industry continues its pace as one of the fastest markets in the world and during the last years, the way clubs interact and engage with the fans has changed significantly due to digital transformation (KPMG, 2018a, pp.3), and the behavior of the new millennial generation. Likewise, fans and football supporters are looking to connect with their clubs and players, that’s why the participation on social media networks has increased, as well as the use of different technologies to enhance a better and personalized customer experience. Considering Real Madrid, as one of the leaders in the industry and the most valuable in terms of digital, how the club will face the fast development of technology to create a closer bonding with the upcoming generations? The current report is structured within five parts to provide an exciting project proposal that might boost the club’s potential, finding a solution to reach this challenging target market. The first part focuses on the situation analysis of the football industry and key industry trends plus an overall overview about Real Madrid (revenue, brand value, fans, digital strategy) introducing a current challenge the club is facing: Santiago Bernabéu renovation. Based on Real Madrid’s stadium case, the second part states the objectives and strategic planning to find a solution for the club through a new revenue stream based on a disruptive technology: the blockchain. For instance, the third part explains this technology and its advantages through a real example. Then, the report introduces the concept that the current project proposes: the Galácticoin for Real Madrid. The idea will be explained in detail, with all its benefits, timeline and the expected revenues. Finally, the document presents the conclusions based on a finance, brand value and fans perspective, according to the project objectives; the team chart description, advisors and references consulted

The Paradoxical Effects of Blockchain Technology on Social Networking Practices

Blockchain technology is a promising, yet not well understood, enabler of large-scale societal and economic change. For instance, blockchain makes it possible for users to securely and profitably share content on social media platforms. In this study, w

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

The impact of COVID-19-related media coverage on the return and volatility connectedness of cryptocurrencies and fiat currencies

This research explores the impact of COVID-19-related media coverage on the dynamic return and volatility connectedness of the three dominant cryptocurrencies (Bitcoin (BTC), Ethereum (ETH) and Ripple (XRP)) and the fiat currencies of the euro, GBP and Chinese yuan. The sample period covers the first and second devasting waves of the COVID-19 pandemic crisis and ranges from January 1, 2020, to December 31, 2020. The dynamic return and volatility connectedness measures are estimated using the time varying parameter-VAR approach. Our return connectedness analysis shows that the media coverage index (only before the first wave) and the cryptocurrencies are the net transmitters of shocks while the fiat currencies are the net receivers of shocks. Similar results are obtained in terms of volatility, except for the euro, which shows a clear net receiver profile in January and February. This fiat currency (the euro) became a net transmitter in March and during the first wave of the COVID-19 crisis, which possibly shows the virulence of the pandemic on the European continent. Moreover, the most relevant differences between the net dynamic (return and volatility) connectedness of these two groups of currencies are focused on the beginning of the sample period, just before the first wave of the SARS-CoV-2 pandemic crisis, although some differences are observed during the first and second waves of the coronavirus outbreak

A dataset of coordinated cryptocurrency-related social media campaigns

The rise in adoption of cryptoassets has brought many new and inexperienced investors in the cryptocurrency space. These investors can be disproportionally influenced by information they receive online, and particularly from social media. This paper presents a dataset of crypto-related bounty events and the users that participate in them. These events coordinate social media campaigns to create artificial "hype" around a crypto project in order to influence the price of its token. The dataset consists of information about 15.8K cross-media bounty events, 185K participants, 10M forum comments and 82M social media URLs collected from the Bounties(Altcoins) subforum of the BitcoinTalk online forum from May 2014 to December 2022. We describe the data collection and the data processing methods employed and we present a basic characterization of the dataset. Furthermore, we discuss potential research opportunities afforded by the dataset across many disciplines and we highlight potential novel insights into how the cryptocurrency industry operates and how it interacts with its audience.Comment: Camera-ready version for the ICWSM 2023 Conference. This paper describes the dataset available at https://zenodo.org/record/781345

A Dataset of Coordinated Cryptocurrency-Related Social Media Campaigns

The rise in adoption of cryptoassets has brought many new and inexperienced investors in the cryptocurrency space. These investors can be disproportionally influenced by information they receive online, and particularly from social media. This paper presents a dataset of crypto-related bounty events and the users that participate in them. These events coordinate social media campaigns to create artificial "hype" around a crypto project in order to influence the price of its token. The dataset consists of information about 15.8K cross-media bounty events, 185K participants, 10M forum comments and 82M social media URLs collected from the Bounties(Altcoins) subforum of the BitcoinTalk online forum from May 2014 to December 2022. We describe the data collection and the data processing methods employed and we present a basic characterization of the dataset. Furthermore, we discuss potential research opportunities afforded by the dataset across many disciplines and we highlight potential novel insights into how the cryptocurrency industry operates and how it interacts with its audience.Comment: Camera-ready version for the ICWSM 2023 Conference. This paper describes the dataset available at https://zenodo.org/record/781345

Studi Netnografi Pola Komunikasi Jaringan Komunitas Cryptocurrency Dogecoin Pada Twitter

Cryptocurrency Dogecoin awalnya dianggap sebagai meme coin namun telah mengalami kenaikan nilai tukar sebanyak 800% pada Januari 2021 dan bertambah lagi sebesar 400% pada April 2021. Hal ini tidak lepas dari dukungan kuat dari komunitas cryptocurrency Dogecoin dan top public profiles pada media sosial Twitter. Penelitian ini menggunakan metode digital netnography untuk melihat pola komunikasi jaringan komunitas cryptocurrency Dogecoin di Twitter. Komunitas yang diteliti tidak terpusat pada akun komunitas tertentu namun meliputi seluruh akun Twitter yang aktif berdiskusi mengenai Dogecoin. Batasan penelitan adalah pada tanggal 1 April - 9 Mei 2021 bertepatan dengan beberapa peristiwa penting yang terjadi. Data yang digunakan adalah semua percakapan pada Twitter dengan kata kunci "Doge" dan diambil menggunakan social network analysis tools Brand24 dan Netlytic. Penelitian ini menemukan adanya 5 tipe interaksi yang merupakan pola komunikasi jaringan Dogecoin. Pola komunikasi yang ditemukan pada penelitian ini dapat memberikan masukan bagi pengembang Dogecoin dan cryptocurrency lainnya tentang pentingnya memberikan informasi yang dapat meyakinkan komunitas untuk tetap hold sebuah cryptocurrency. Kemudian pentingnya membina komunitas yang saling mendukung dan memberi semangat di antara anggota komunitas, dan pentingnya bekerjasama dengan top public profiles untuk memberikan keyakinan dan konfirmasi untuk mengatasi keresahan komunitas terkait volatility yang tinggi dari sebuah cryptocurrency

Predicting Information Diffusion on Social Media

Sotsiaalmeedia on saanud moodsa elu osaks. Pidevalt tekib juurde informatsiooni, mida maailmaga jagatakse. Informatsiooni hajumist on varasemalt uuritud paljude teadlaste poolt, kuna sel on rakendusi erinevates valdkondades, nagu näiteks sotsiaalmeediaturundamine ja uudiste levimise uurimine. Informatsiooni leviku kiirust mõjutab selle olulisus inimestele. Käesolevas töös uuritakse info hajumist sotsiaalvõrgustikus ja ennustatakse sisu populaarsust kasutades juhendatud masinõppe algoritme. Kolme Twitterist pärit andmestikku analüüsitakse ja kasutatakse erinevate masinõppe mudelite konstrueerimiseks.Defineerisime säutsu populaarsuse kui taaspostituste arvu, mida iga originaalsäuts sai, ning püstitasime uurimisprobleemid binaarsete ja mitmeklassiliste ennustusülesannetena. Uurisime, kuidas esialgne säutsude taaspostitamise käitumine mõjutab mudelite ennustusvõimekust. Lisaks analüüsisime, kas viimase tunni taaspostituskäitumine aitab ennustada taas-postituskäitumist järgneva tunni jooksul. Täiendav tähelepanu oli suunatud ka ennustuseks tähtsate tunnuste leidmiseks.Binaarse ennustuse puhul näitasid mudelid tulemusi AUC (area under curve) kuni 95% ning F1-skoori kuni 87%. Mitmeklassiliste ennustuste puhul suutsid mudelid saavutada kuni 60% üldise täpsuse ning F1-skoori kuni 67%. Paremad ennustustäpsused saavutati siis, kui postitustel olid väga madalad või väga kõrged taaspostituste arvud. Me genereerisime mudelid kasutades üht andmestikku ning testisime neid ülejäänud kahe peal. See näitas, et mudelid on piisavalt robustsed, et tegeleda erinevate teemadega.Social media has become a part of the everyday life of modern society. A lot of infor-mation is created and shared with the world continuously. Predicting information has been studied in the past by many researchers since it has its applications in various domains such as viral marketing, news propagation etc.Some information spreads faster compared to others depending on what interests people. In this thesis, by using supervised machine learning algorithms, we studied information diffusion in a social network and predicted content popularity. Three datasets from Twitter are collected and analysed for building and testing various models based on different ma-chine learning algorithms.We defined tweet popularity as number of retweets any original message received and stated our research problems as binary and multiclass prediction tasks. We investigated how initial retweeting behaviour of a message affects the predictive power of a model. We also analysed if a recent one-hour retweeting behaviour can help to predict a tweet popu-larity of the following hour. Besides that, main focus is made on finding features im-portant for the prediction.For binary prediction, the models showed performance of AUC up to 95% and F1 up to 87%. For multiclass prediction, the models were able to predict up to 60% of overall accu-racy and 67% of F1, with more accurate performance of classes with messages with very low and high retweet counts comparing to others. We created our models using one da-taset and tested our approach on the other two datasets, which showed that the models are robust enough to deal with multiple topics