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36 results for “cryptocurrency”
Data for: Connectedness and spillover effect between cryptocurrency and financial assets
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Dataset on the online cryptocurrency discussion on Twitter, Telegram, and Discord
<p>This Dataset is described in <em><strong>Charting the Landscape of Online Cryptocurrency Manipulation</strong></em>. <strong><em>IEEE Access (2020)</em></strong>, a study that aims to map and assess the extent of cryptocurrency manipulations within and across the online ecosystems of Twitter, Telegram, and Discord. Starting from tweets mentioning cryptocurrencies, we leveraged and followed invite URLs from platform to platform, building the invite-link network, in order to study the invite link diffusion process.</p> <p>Please, refer to the paper below for more details.</p> <p>Nizzoli, L., Tardelli, S., Avvenuti, M., Cresci, S., Tesconi, M. & Ferrara, E. (2020). Charting the Landscape of Online Cryptocurrency Manipulation. IEEE Access (2020).</p> <p>This dataset is composed of: </p> <ul> <li>~16M tweet ids shared between March and May 2019, mentioning at least one of the 3,822 cryptocurrencies (cashtags) provided by the CryptoCompare public API;</li> <li>~13k nodes of the invite-link network, i.e., the information about the Telegram/Discord channels and Twitter users involved in the cryptocurrency discussion (e.g., id, name, audience, invite URL);</li> <li>~62k edges of the invite-link network, i.e., the information about the flow of invites (e.g., source id, target id, weight).</li> </ul> <p>With such information, one can easily retrieve the content of channels and messages through Twitter, Telegram, and Discord public APIs.</p> <p>Please, refer to the README file for more details about the fields.</p>
Publikasi Ilmiah Penerapan Cryptocurrency di Indonesia (2020 - 2024)
<p>Dataset publikasi ilmiah cryptocurrency di Indonesia dalam rentang 2020 - 2024</p>
Cryptocurrency Trading and Its Relationship with Other Addictions Among Healthcare Professionals: Cross Sectional Study
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CRYPTOCURRENCIES AND DIGITAL CURRENCIES: NAVIGATING THE INTERSECTION OF INNOVATION AND CYBERSECURITY
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Forecasting Cryptocurrency Markets: Predictive Modelling Using Statistical and Machine Learning Approaches
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Connectedness and spillover effect between cryptocurrency and financial assets
Cryptocurrencies have quickly become one type of important financial asset. Accordingly, it is important to understand the interaction between cryptocurrency and other financial asset markets. However, previous literature paid less attention to the correlation between the price trend of cryptocurrencies and other financial assets. Using the vector autoregression model, we analyzed price correlation and spillover effect between cryptocurrencies and financial assets between November 2017 and February 2022. The study concludes that stock price has a spillover effect on cryptocurrencies, government bonds, and precious metals. The research results are useful while allocating portfolios or hedge strategies that include cryptocurrencies and financial assets such as stocks, government bonds, and precious metals.
Binance cryptocurrencies historical daily data
<p>Binance cryptocurrencies historical daily data</p>
Data set for Exploring Machine Learning-Based Methods for anomalies detection: Evidence from cryptocurrencies
<p><strong>Exploring Machine Learning-Based Methods for anomalies detection: Evidence from cryptocurrencies</strong></p>
Data from: Evolutionary dynamics of the cryptocurrency market
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Data from: Buzz factor or innovation potential: what explains cryptocurrencies' returns?
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Data from: Critical slowing down associated with critical transition and risk of collapse in cryptocurrency
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Data from: Classification of cryptocurrency coins and tokens by the dynamics of their market capitalisations
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Connectedness and spillover effect between cryptocurrency and financial assets
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PAN23 Profiling Cryptocurrency Influencers with Few-shot Learning
<p>This is the dataset for the shared task on <a href="https://pan.webis.de/clef23/pan23-web/author-profiling.html#">Profiling Cryptocurrency Influencers with Few-shot Learning</a>. Please consult the task's page for further details on the format, the dataset's creation, and links to baselines and utility code.</p> <p> </p> <p><strong>Task</strong>: In this shared task we aim to profile cryptocurrency influencers in social media, from a low-resource perspective. Moreover, we propose to categorize other related aspects of the influencers, also using a low-resource setting. Specifically, we focus on English Twitter posts for three different sub-tasks:</p> <ol> <li><strong>Low-resource influencer profiling (subtask1):</strong> <ul> <li>Input:<br> 32 users per label with a maximum of 10 English tweets each.<br> Classes: (1) null, (2) nano, (3) micro, (4) macro, (5) mega</li> <li>Official evaluation metric: Macro F1</li> <li>Submission: TIRA.</li> <li>Baselines: User-character Logistic Regression; <a href="https://huggingface.co/sentence-transformers/sentence-t5-large">t5-large</a> (bi-encoders) - zero shot [7], <a href="https://huggingface.co/sentence-transformers/sentence-t5-large">t5-large</a> (label tuning) - few shot [7]</li> </ul> </li> <li><strong>Low-resource influencer interest identification (subtask2):</strong> <ul> <li>Input:<br> 64 users per label with 1 English tweet each.<br> Classes: (1) technical information, (2) price update, (3) trading matters, (4) gaming, (5) other</li> <li>Official evaluation metric: Macro F1</li> <li>Submission: TIRA.</li> <li>Baselines: User-character Logistic Regression; <a href="https://huggingface.co/sentence-transformers/sentence-t5-large">t5-large</a> (bi-encoders) - zero shot [7], <a href="https://huggingface.co/sentence-transformers/sentence-t5-large">t5-large</a> (label tuning) - few shot [7]</li> </ul> </li> <li><strong>Low-resource influencer intent identification (subtask3):</strong> <ul> <li>Input:<br> 64 users per label with 1 English tweets each.<br> Classes: (1) subjective opinion, (2) financial information, (3) advertising, (4) announcement</li> <li>Official evaluation metric: Macro F1</li> <li>Submission: TIRA.</li> <li>Baselines: User-character Logistic Regression; <a href="https://huggingface.co/sentence-transformers/sentence-t5-large">t5-large</a> (bi-encoders) - zero shot [7], <a href="https://huggingface.co/sentence-transformers/sentence-t5-large">t5-large</a> (label tuning) - few shot [7]</li> </ul> </li> </ol> <p><strong>Versioning:</strong> </p> <ul> <li>1.0: initial upload</li> <li>1.1 fixed a minor bug where some users contained some non-English text. Since English is the target language in the competition, all non-English texts have been replaced or removed. </li> </ul>
Exploring Machine Learning-Based Methods for anomalies detection: Evidence from cryptocurrencies returns
<p>The data consists of 4500 observation for each of the 6 cryptocurrencies.</p>
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OpenNeuro
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