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method/Readme.md
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### Data
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#### On-chain data
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We utilize Google BigQuery to extract Ethereum's blockchain data, including timestamps, block numbers, hashes, parent hashes, transactions, etc. We retain only the pertinent features to predict gas usage in forthcoming blocks: timestamp, gas limit, gas used, and base fee. We exclude other variables, such as transaction numbers, despite their high correlation with gas usage, based on our specific research focus. Furthermore, our study acknowledges the impact of token airdrops on transaction engagement levels for recipients and non-recipients. According to Guo\cite{guo2023spillover}, token airdrops can significantly influence engagement, resulting in pronounced gas usage volatility and subsequent base fee fluctuations. Consequently, our analysis is bifurcated into two distinct periods. The first period examines the ARB token airdrop, the most substantial airdrop event in 2023, which occurred from March 21 to April 1 and comprised 78,290 blocks. The second period, devoid of significant fungible token airdrop activities, extends from June 1, 2023, to July 1, 2023, encompassing 213,244 blocks. This temporal delineation allows for a comprehensive analysis of the effects of significant airdrop events on Ethereum's gas dynamics.
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#### Off-chain data
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Another dataset is users' discussion text from Discord. Discord hosts vibrant crypto discussions ranging from market analysis to technical debates, yet remains underexplored for sentiment analysis, unlike platforms like Twitter and Reddit, where extensive studies in cryptocurrency sentiment research exist \cite{kraaijeveld2020predictive,mohapatra2019kryptooracle,khan2022business}. We focus on critical communities such as Binance, Uniswap, and the Ethereum Dev channels on Discord. These are the communities for the largest centralized exchange, decentralized exchanges, and Ethereum developers, respectively. Analyzing sentiments from these communities, which influence Ethereum's network activity and gas usage through trading dynamics and developer engagement, provides crucial insights for predicting future gas demand and formulating effective network management strategies. The discussion texts are queries from Discord using the DiscordChatExporter, an open-source tool on GitHub.
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### Data
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#### On-chain data
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We utilize Google BigQuery to extract Ethereum's blockchain data, including timestamps, block numbers, hashes, parent hashes, transactions, etc. We retain only the pertinent features to predict gas usage in forthcoming blocks: timestamp, gas limit, gas used, and base fee. We exclude other variables, such as transaction numbers, despite their high correlation with gas usage, based on our specific research focus. Furthermore, our study acknowledges the impact of token airdrops on transaction engagement levels for recipients and non-recipients. According to Guo\cite{guo2023spillover}, token airdrops can significantly influence engagement, resulting in pronounced gas usage volatility and subsequent base fee fluctuations. Consequently, our analysis is bifurcated into two distinct periods. The first period examines the ARB token airdrop, the most substantial airdrop event in 2023, which occurred from March 21 to April 1 and comprised 78,290 blocks. The second period, devoid of significant fungible token airdrop activities, extends from June 1, 2023, to July 1, 2023, encompassing 213,244 blocks. This temporal delineation allows for a comprehensive analysis of the effects of significant airdrop events on Ethereum's gas dynamics.
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# Operational Measures
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## Variables
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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</head>
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<body>
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<table>
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<caption>Variable Description</caption>
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<tr>
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<th>Variable Name</th>
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<th>Description</th>
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<th>Unit</th>
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<th>Type</th>
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</tr>
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<tr>
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<td>timestamp</td>
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<td>Recording of the time of each block</td>
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<td></td>
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<td>String</td>
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</tr>
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<tr>
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<td>number</td>
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<td>The number of blocks on the chain</td>
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<td></td>
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<td>Numeric</td>
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</tr>
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<tr>
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<td>gas_used</td>
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<td>Actual Gas Used</td>
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<td>Gwei</td>
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<td>Numeric</td>
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</tr>
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<tr>
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<td>gas_limit</td>
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<td>The maximum allowed gas per block</td>
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<td>Gwei</td>
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<td>Numeric</td>
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</tr>
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<tr>
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<td>base_fee_per_gas</td>
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<td>The base fee set for each block</td>
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<td>Ether</td>
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<td>Numeric</td>
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</tr>
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<tr>
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<td>gas_fraction</td>
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<td>Fraction between Gas Used and Gas Limit</td>
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<td></td>
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<td>Numeric</td>
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</tr>
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<tr>
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<td>gas_target</td>
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<td>The optimal gas used for each block</td>
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<td></td>
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<td>Numeric</td>
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</tr>
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<tr>
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<td>Y</td>
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<td>Normalized Gas Used</td>
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<td></td>
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<td>Numeric</td>
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</tr>
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<tr>
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<td>$Y_t$</td>
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<td>Response variable equals to the gas_fraction</td>
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<td></td>
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<td>Numeric</td>
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</tr>
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</table>
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</body>
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</html>
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#### Off-chain data
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Another dataset is users' discussion text from Discord. Discord hosts vibrant crypto discussions ranging from market analysis to technical debates, yet remains underexplored for sentiment analysis, unlike platforms like Twitter and Reddit, where extensive studies in cryptocurrency sentiment research exist \cite{kraaijeveld2020predictive,mohapatra2019kryptooracle,khan2022business}. We focus on critical communities such as Binance, Uniswap, and the Ethereum Dev channels on Discord. These are the communities for the largest centralized exchange, decentralized exchanges, and Ethereum developers, respectively. Analyzing sentiments from these communities, which influence Ethereum's network activity and gas usage through trading dynamics and developer engagement, provides crucial insights for predicting future gas demand and formulating effective network management strategies. The discussion texts are queries from Discord using the DiscordChatExporter, an open-source tool on GitHub.
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