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Wray warns of Hamas-inspired terror attacks while his FBI has been busy chasing Trump supporters

The incident took place late in the evening last week at Happy Day Food Market & Deli in the city's Kingsessing neighborhood.

Philly thug picked the wrong convenience store to knock off

GOP lawmakers are seeking to get a 14.3 billion aid package to Israel but want to offset the money with cuts to Joe Biden's beefed-up IRS.

House GOP introduces $14.3 billion Israel funding bill that uses money from Biden’s beefed-up IRS

Former Vice President Mike Pence unexpectedly suspended his White House campaign, ending a bid that failed to catch fire with Republican voters. Pence made his announcement during […]

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Disgraced former New York Gov. Andrew Cuomo told Bill Maher on Friday that he believes President Joe Biden should be primaried.

Andrew Cuomo calls for Biden to face primary, says he’d ‘probably’ be running if scandals didn’t drive him from office

Gettr

gettr

Build a Receipt Generator with Just the Rootstock API and RPC Method

Creeu un generador de rebuts només amb l'API d'arreel i el mètode RPC

Δημιουργήστε μια γεννήτρια αποδείξεων μόνο με τη μέθοδο Rootstock API και RPC

Cree un generador de recibos con solo la API de Rootstock y el método RPC

Créez un générateur de reçus avec uniquement l'API Rootstock et la méthode RPC

Izgradite generator potvrda samo s Rootstock API-jem i RPC metodom

Costruisci un generatore di ricevute solo con l'API Rootstock e il metodo RPC

Rootstock API と RPC メソッドだけで領収書ジェネレーターを構築する

Тек Rootstock API және RPC әдісі арқылы түбіртек генераторын жасаңыз

Создайте генератор квитанций, используя только API Rootstock и метод RPC

Unda Jenereta ya Risiti kwa kutumia API ya Mizizi tu na Mbinu ya RPC

Генератори квитансияро бо танҳо API Rootstock ва усули RPC созед

Yakha i-Generator yerisidi nge-Just the Rootstock API kanye ne-RPC Method

Building a Production Grade Testnet Faucet With Typescript, Redis and Nextjs

How to Build a Telegram Bot That Queries Rootstock Data Using Rootstock RPC API

Hey there! I have been for a long time in dApps developement and I know on my own how it is difficult to get started with blockchain APIs. Especially with their endpoints. I decided to gather the most important endpoints for Ethereum, Binance, Bitcoin, Ploygon, and Solana blockchains in this article. I tried to be consistent and to the point. Hope they will help you! Ethereum API The Ethereum API is fundamental for developers working on the Ethereum network. It provides access to blockchain data, enabling interaction with smart contracts and account management. Key endpoints include: eth_call. Executes a message call immediately without creating a transaction.
eth_getTransactionByHash. Retrieves transaction details using its hash.
eth_getBlockByNumber. Returns block information by its number.
eth_getCode. Retrieves the code at a specific address, useful for checking smart contracts' bytecode.
eth_getLogs. Fetches logs from smart contracts, often used to track events like token transfers.
eth_estimateGas. Estimates the gas required to execute a transaction, helping developers calculate costs.
eth_gasPrice. Returns the current gas price in the network, essential for setting transaction fees.
eth_getTransactionCount. Retrieves the number of transactions sent from an address, often used to get the nonce for new transactions. These endpoints are imperative for building dApps that require real-time blockchain data. I mean DeFi platforms and NFT marketplaces. For example, eth_call is used to query token balances in smart contracts without broadcasting a transaction. Solana API Solana's API is designed for high-speed, scalable applications. This is why Solana is a popular choice for developers focused on performance. Key endpoints include: getRecentBlockhash. Fetches the latest block hash needed for signing transactions.
getAccountInfo. Retrieves account data like balance and ownership.
sendTransaction. Submits a transaction to the network.
getConfirmedBlock. Retrieves information about a specific confirmed block, including all transactions.
getConfirmedSignaturesForAddress2. Fetches signatures for confirmed transactions associated with an address.
getSupply. Provides the total supply of SOL, the native token of Solana.
getProgramAccounts. Returns all accounts owned by a particular program (smart contract).
getTokenAccountsByOwner. Retrieves token accounts owned by a specific public key, useful for querying balances of various tokens. These endpoints support Solana’s proof-of-history (PoH) system, which timestamps transactions to ensure fast processing. For instance, sendTransaction is imperative for executing trades on DEXs built on Solana. There, low latency is crucial. Bitcoin API Bitcoin's API provides essential tools for interacting with the Bitcoin blockchain. They focuse on transaction and wallet management. Key endpoints include: getblockchaininfo. Retrieves the current blockchain state, including block count and difficulty.
getrawtransaction. Retrieves detailed transaction information using its ID.
getbalance. Returns the balance of a specified wallet address.
listunspent. Lists unspent transaction outputs (UTXOs) for a set of addresses, essential for constructing new transactions.
sendrawtransaction. Submits a raw transaction (created and signed offline) to the Bitcoin network.
getmempoolinfo. Provides information about the nodes' memory pool (mempool), where unconfirmed transactions are held.
getblockhash. Retrieves the block hash of a block based on its height, useful for navigating the blockchain.
decoderawtransaction. Decodes a raw transaction hex string into a readable JSON object, helping to understand transaction details before broadcasting. These endpoints are standard in Bitcoin’s JSON-RPC API and are used in various Bitcoin-related development tasks. The getrawtransaction endpoint, for example, is used to verify transaction details before confirmation in a block. Polygon API Polygon offers a Layer 2 scaling solution for Ethereum, optimized for lower transaction costs and higher throughput. Key endpoints include: eth_blockNumber. Retrieves the most recent block number.
eth_sendTransaction. Sends a transaction to the network with significantly lower gas fees.
eth_getLogs. Fetches event logs from the blockchain, useful for tracking smart contract events.
eth_getBlockByHash. Fetches block information using the block hash, similar to eth_getBlockByNumber but identified by hash.
eth_getUncleByBlockHashAndIndex. Retrieves an uncle block by hash and index position, used in block reward calculations.
eth_getStorageAt. Returns the value from a storage position at a specific address, useful for reading smart contract state.
eth_newFilter. Creates a filter object to listen for events on the blockchain, such as new blocks or transactions.
eth_uninstallFilter. Removes a filter created with eth_newFilter, stopping event listening to reduce resource usage. These APIs are commonly used in dApps that need Ethereum compatibility but require enhanced performance, particularly in applications dealing with microtransactions where minimizing costs is needed. Binance Smart Chain (BNB) API Binance Smart Chain (BSC) runs parallel to Binance Chain, offering smart contract functionality with Ethereum Virtual Machine (EVM) compatibility. Key endpoints include: bnb_getTransactionReceipt. Retrieves the receipt of a transaction, including status, gas used, and logs.
bnb_call. Executes a message call without creating a transaction, similar to Ethereum’s eth_call.
bnb_getBlockByHash. Returns block information based on the block hash.
bnb_getLogs. Fetches logs from BSC smart contracts, similar to Ethereum’s eth_getLogs, and is crucial for event tracking.
bnb_getCode. Retrieves the code at a specific address, helpful for verifying deployed smart contracts.
bnb_estimateGas. Estimates the gas required for a transaction, aiding in cost prediction on BSC.
bnb_getStorageAt. Fetches storage data from a specific contract address, allowing for state reading in smart contracts.
bnb_getTransactionCount. Returns the number of transactions sent from an address, necessary for setting the nonce in new transactions. These endpoints are must-have things for developers building on BSC. Especially those migrating from Ethereum. The bnb_getTransactionReceipt endpoint, for instance, is used to confirm transaction success before updating user interfaces. Hey there! I have been for a long time in dApps developement and I know on my own how it is difficult to get started with blockchain APIs. Especially with their endpoints. I decided to gather the most important endpoints for Ethereum, Binance, Bitcoin, Ploygon, and Solana blockchains in this article. I tried to be consistent and to the point. Hope they will help you! Ethereum API The Ethereum API is fundamental for developers working on the Ethereum network. It provides access to blockchain data, enabling interaction with smart contracts and account management. Key endpoints include: eth_call. Executes a message call immediately without creating a transaction. eth_getTransactionByHash. Retrieves transaction details using its hash. eth_getBlockByNumber. Returns block information by its number. eth_getCode. Retrieves the code at a specific address, useful for checking smart contracts' bytecode. eth_getLogs. Fetches logs from smart contracts, often used to track events like token transfers. eth_estimateGas. Estimates the gas required to execute a transaction, helping developers calculate costs. eth_gasPrice. Returns the current gas price in the network, essential for setting transaction fees. eth_getTransactionCount. Retrieves the number of transactions sent from an address, often used to get the nonce for new transactions. eth_call. Executes a message call immediately without creating a transaction. eth_getTransactionByHash. Retrieves transaction details using its hash. eth_getBlockByNumber. Returns block information by its number. eth_getCode. Retrieves the code at a specific address, useful for checking smart contracts' bytecode. eth_getLogs. Fetches logs from smart contracts, often used to track events like token transfers. eth_estimateGas. Estimates the gas required to execute a transaction, helping developers calculate costs. eth_gasPrice. Returns the current gas price in the network, essential for setting transaction fees. eth_getTransactionCount. Retrieves the number of transactions sent from an address, often used to get the nonce for new transactions. These endpoints are imperative for building dApps that require real-time blockchain data. I mean DeFi platforms and NFT marketplaces. For example, eth_call is used to query token balances in smart contracts without broadcasting a transaction. eth_call Solana API Solana's API is designed for high-speed, scalable applications. This is why Solana is a popular choice for developers focused on performance. Key endpoints include: getRecentBlockhash. Fetches the latest block hash needed for signing transactions. getAccountInfo. Retrieves account data like balance and ownership. sendTransaction. Submits a transaction to the network. getConfirmedBlock. Retrieves information about a specific confirmed block, including all transactions. getConfirmedSignaturesForAddress2. Fetches signatures for confirmed transactions associated with an address. getSupply. Provides the total supply of SOL, the native token of Solana. getProgramAccounts. Returns all accounts owned by a particular program (smart contract). getTokenAccountsByOwner. Retrieves token accounts owned by a specific public key, useful for querying balances of various tokens. getRecentBlockhash. Fetches the latest block hash needed for signing transactions. getAccountInfo. Retrieves account data like balance and ownership. sendTransaction. Submits a transaction to the network. getConfirmedBlock. Retrieves information about a specific confirmed block, including all transactions. getConfirmedSignaturesForAddress2. Fetches signatures for confirmed transactions associated with an address. getSupply. Provides the total supply of SOL, the native token of Solana. getProgramAccounts. Returns all accounts owned by a particular program (smart contract). getTokenAccountsByOwner. Retrieves token accounts owned by a specific public key, useful for querying balances of various tokens. These endpoints support Solana’s proof-of-history (PoH) system, which timestamps transactions to ensure fast processing. For instance, sendTransaction is imperative for executing trades on DEXs built on Solana. There, low latency is crucial. sendTransaction Bitcoin API Bitcoin's API provides essential tools for interacting with the Bitcoin blockchain. They focuse on transaction and wallet management. Key endpoints include: getblockchaininfo. Retrieves the current blockchain state, including block count and difficulty. getrawtransaction. Retrieves detailed transaction information using its ID. getbalance. Returns the balance of a specified wallet address. listunspent. Lists unspent transaction outputs (UTXOs) for a set of addresses, essential for constructing new transactions. sendrawtransaction. Submits a raw transaction (created and signed offline) to the Bitcoin network. getmempoolinfo. Provides information about the nodes' memory pool (mempool), where unconfirmed transactions are held. getblockhash. Retrieves the block hash of a block based on its height, useful for navigating the blockchain. decoderawtransaction. Decodes a raw transaction hex string into a readable JSON object, helping to understand transaction details before broadcasting. getblockchaininfo. Retrieves the current blockchain state, including block count and difficulty. getrawtransaction. Retrieves detailed transaction information using its ID. getbalance. Returns the balance of a specified wallet address. listunspent. Lists unspent transaction outputs (UTXOs) for a set of addresses, essential for constructing new transactions. sendrawtransaction. Submits a raw transaction (created and signed offline) to the Bitcoin network. getmempoolinfo. Provides information about the nodes' memory pool (mempool), where unconfirmed transactions are held. getblockhash. Retrieves the block hash of a block based on its height, useful for navigating the blockchain. decoderawtransaction. Decodes a raw transaction hex string into a readable JSON object, helping to understand transaction details before broadcasting. These endpoints are standard in Bitcoin’s JSON-RPC API and are used in various Bitcoin-related development tasks. The getrawtransaction endpoint, for example, is used to verify transaction details before confirmation in a block. getrawtransaction Polygon API Polygon offers a Layer 2 scaling solution for Ethereum, optimized for lower transaction costs and higher throughput. Key endpoints include: eth_blockNumber. Retrieves the most recent block number. eth_sendTransaction. Sends a transaction to the network with significantly lower gas fees. eth_getLogs. Fetches event logs from the blockchain, useful for tracking smart contract events. eth_getBlockByHash. Fetches block information using the block hash, similar to eth_getBlockByNumber but identified by hash. eth_getUncleByBlockHashAndIndex. Retrieves an uncle block by hash and index position, used in block reward calculations. eth_getStorageAt. Returns the value from a storage position at a specific address, useful for reading smart contract state. eth_newFilter. Creates a filter object to listen for events on the blockchain, such as new blocks or transactions. eth_uninstallFilter. Removes a filter created with eth_newFilter, stopping event listening to reduce resource usage. eth_blockNumber. Retrieves the most recent block number. eth_sendTransaction. Sends a transaction to the network with significantly lower gas fees. eth_getLogs. Fetches event logs from the blockchain, useful for tracking smart contract events. eth_getBlockByHash. Fetches block information using the block hash, similar to eth_getBlockByNumber but identified by hash. eth_getUncleByBlockHashAndIndex. Retrieves an uncle block by hash and index position, used in block reward calculations. eth_getStorageAt. Returns the value from a storage position at a specific address, useful for reading smart contract state. eth_newFilter. Creates a filter object to listen for events on the blockchain, such as new blocks or transactions. eth_uninstallFilter. Removes a filter created with eth_newFilter, stopping event listening to reduce resource usage. These APIs are commonly used in dApps that need Ethereum compatibility but require enhanced performance, particularly in applications dealing with microtransactions where minimizing costs is needed. Binance Smart Chain (BNB) API Binance Smart Chain (BSC) runs parallel to Binance Chain, offering smart contract functionality with Ethereum Virtual Machine (EVM) compatibility. Key endpoints include: bnb_getTransactionReceipt. Retrieves the receipt of a transaction, including status, gas used, and logs. bnb_call. Executes a message call without creating a transaction, similar to Ethereum’s eth_call. bnb_getBlockByHash. Returns block information based on the block hash. bnb_getLogs. Fetches logs from BSC smart contracts, similar to Ethereum’s eth_getLogs, and is crucial for event tracking. bnb_getCode. Retrieves the code at a specific address, helpful for verifying deployed smart contracts. bnb_estimateGas. Estimates the gas required for a transaction, aiding in cost prediction on BSC. bnb_getStorageAt. Fetches storage data from a specific contract address, allowing for state reading in smart contracts. bnb_getTransactionCount. Returns the number of transactions sent from an address, necessary for setting the nonce in new transactions. bnb_getTransactionReceipt. Retrieves the receipt of a transaction, including status, gas used, and logs. bnb_call. Executes a message call without creating a transaction, similar to Ethereum’s eth_call. bnb_getBlockByHash. Returns block information based on the block hash. bnb_getLogs. Fetches logs from BSC smart contracts, similar to Ethereum’s eth_getLogs, and is crucial for event tracking. bnb_getCode. Retrieves the code at a specific address, helpful for verifying deployed smart contracts. bnb_estimateGas. Estimates the gas required for a transaction, aiding in cost prediction on BSC. bnb_getStorageAt. Fetches storage data from a specific contract address, allowing for state reading in smart contracts. bnb_getTransactionCount. Returns the number of transactions sent from an address, necessary for setting the nonce in new transactions. These endpoints are must-have things for developers building on BSC. Especially those migrating from Ethereum. The bnb_getTransactionReceipt endpoint, for instance, is used to confirm transaction success before updating user interfaces. bnb_getTransactionReceipt

Key Endpoints For Blockchain APIs: Ethereum, Bitcoin, Polygon, Binance Smart Chain, And Solana

The Systemic Impact of Deplatforming on Social Media: Acknowledgements, Data, and References

Authors:
(1) Amin Mekacher, City University of London, Department of Mathematics, London EC1V 0HB, (UK) and this author contributed equally;
(2) Max Falkenberg, City University of London, Department of Mathematics, London EC1V 0HB, (UK), this author contributed equally, and Corresponding authors: max.falkenberg@city.ac.uk;
(3) Andrea Baronchelli, City University of London, Department of Mathematics, London EC1V 0HB, (UK), The Alan Turing Institute, British Library, London NW1 2DB, (UK), and Corresponding authors: abaronchelli@turing.ac.uk. Table of Links Abstract and Introduction Results Discussion & Conclusion Methods Acknowledgements, Data availability, and References METHODS Gettr data The data used for this study has been collected using GoGettr [76], a public client developed by the Stanford Internet Observatory to give access to the Gettr API. This API allows to query user interactions, including the posts they like or share. User profiles were initially collected through a snowball sampling, by using highly popular accounts on the platform as seed users, and using the API to query their follower and following list, before repeating the same process for a random sample of the newly retrieved users. Repeating this process many times ensures that our dataset is near-complete for the studied time period. To attract more users from Twitter, Gettr previously offered a feature that would automatically import a user’s tweets upon creating an account. However, due to Twitter blocking this capability on July 10, 2021[77], Gettr had to discontinue this feature. To ensure the accuracy of our results, any posts imported before July 10, 2021, and any Gettr post whose timestamp precedes the account’s creation date were removed from our dataset. To ensure our case study on the Brazilian rightwing group encompasses the Bras´ılia insurrection, we expanded the data collection time frame for any user associated with the Brazilian community. The data collection was run in July 2022 for every user whose profile we have retrieved, and in January 2023 for the users in the Brazilian cohort. Twitter data For each verified Gettr account where the Gettr API references their Twitter followers in the account metadata, we check that the Twitter account with the same username is active using the Twitter API (see https://developer.twitter.com/en/ products/twitter-api/academic-research). Then, for each active account we download their Twitter timeline including all tweets and retweets in the period July 2021 to May 2022. This totals approximately 12 million tweets. Data was collected between September and October 2022, preceding Elon Musk’s amnesty of suspended Twitter accounts. User labelling Throughout our analysis, we label verified Gettr users as being either “matched” or “banned”, depending on whether their corresponding Twitter account is active or suspended. Any verified user who decided to link their Twitter account on their Gettr page has their Twitter follower count displayed on their profile, which can also be retrieved from the Gettr API. We stress that this selfdeclaration permits cross-platform matching since users can “reasonably expect” that their Gettr accounts will be associated with their Twitter accounts. To match accounts across platforms, we assumed that users picked the same username on both Gettr and Twitter, and we used the Twitter API to retrieve their Twitter activity. A user is classified as “banned” if the Twitter user endpoint returned an error indicating that the account has been suspended. Note that for data privacy reasons, all analysis of users across platforms is aggregated at the cohort level; we do not present results for individual users. Calculating account survival The Kaplan-Meier estimate is a tool used to quantify the survival rate of a population (in our case, users active on a social platform) over time. For each time step t, we measure how many users become indefinitely inactive, and we quantify the survival rate as Topic Modelling Gettr and Twitter content is analysed using the BERTopic topic modelling library [32]. This method extracts latent topics from an ensemble of documents (in our case Gettr posts and Twitter tweets). The base model uses pre-trained transformer-based language models to construct document embeddings which are then clustered. These methods are known to struggle with very short documents which are common on micro-blogging sites. Hence, we train our topic model using exclusively Gettr posts which are longer than 100 characters. To avoid any single user dominating a specific topic, we limit the training set to no more than 50 posts from any given user. Latent ideology To calculate the latent ideology on Gettr, we use the method developed in [36, 37] and filtering procedures from [38]. The method uses a bipartite approach where it classifies Gettr accounts as influencers or regular users. It then generates an ordering of users based on the interaction patterns of regular users with the influencer set. In the current paper, we select the matched and banned user sets as our influencers, and the remaining set of Gettr users as our cohort of regular users. Two factors can conflate the latent ideology: (1) account geography, and (2) a lack of user-influencer interactions. Since we are interested in the segregation of the banned and matched cohorts based on political ideology, the former is problematic because country-specific communities on social media can appear structurally segregated from a related community in other countries, even if they are politically aligned. For this reason, we remove a small number of accounts associated with the UK and China from our set of Gettr influencers. In the latter case, a lack of user-influencer interactions can be problematic since influencers with few user interactions appear as erroneous outliers when computing the latent ideology, often because they do not take active part in the conversation. Hence, we restrict our influencer set to the 500 banned and matched accounts who receive the largest number of interactions from other users on Gettr. In the current study, we consider any interaction type including comments, shares and likes. The latent ideology is robust as long as the number of influencers used is larger than 200 accounts [38]. In order to assess the modality of the ideology distributions, we use Hartigan’s diptest. This approach is used to identify polarized social media conversations and echo-chambers [37, 38]. Hartigan’s diptest compares a test distribution against a matched unimodal distribution to assess distribution modality [39]. The test computes the distance, D, between the cumulative density of the test distribution and the cumulative density of the matched unimodal distribution. The D-statistic is accompanied by a p-value which quantifies whether the test distribution is significantly different to a matched unimodal distribution. A p-value of less than 0.01 indicates a multimodal distribution. Toxicity analysis The toxicity of Gettr and Twitter content is computed using the Google Perspective API [42], which has been used in several social media studies to assess platform toxicity [78–80]. Given a text input, the API returns a score between 0 and 1, indicating how likely a human moderator is to flag the text as being toxic. For our analysis, we used the flagship attribute “toxicity”, which is defined as “[a] rude, disrespectful, or unreasonable comment that is likely to make people leave a discussion” [81]. When computing statistics for the toxicity of each user cohort, we apply a bootstrapping procedure to avoid erroneous results from variable cohort sizes. This is important since the distribution of post toxicity is fat tailed; there are far more posts with low toxicity than high toxicity. Therefore, a smaller set of posts from a user cohort may have a lower median toxicity, purely due to sampling effects. To avoid this conflation, bootstrapping is employed where equally sized samples are drawn from each cohort (usually 100 posts), and the median toxicity is computed for each sample. Then, the sampling procedure is repeated 100 times to compute the median and inter-quartile range for the sampled post toxicity. News media classification using Media Bias / Fact Check For the quote-ratio analysis in Fig. 5, we identify the Twitter handles of news media outlets and classify their political leaning using Media Bias / Fact Check (MBFC; see https://mediabiasfactcheck.com/). Ratings provided by MBFC are largely similar to other reputable media rating datasets [60]. MBFC classify news outlets under seven leaning categories: extreme left, left, center-left, least (media considered unbiased), center-right, right, extreme right. To ensure that we have enough news media outlets to enable the quote-ratio analysis, we group these classifications into four larger groups: Left – (extreme left, left, center-left), Least – (least), Right – (center-right, right), Far-right – (extreme right). Note that we have chosen to use the terminology “far-right” instead of “extremeright” since the former is more common in the academic literature. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Amin Mekacher, City University of London, Department of Mathematics, London EC1V 0HB, (UK) and this author contributed equally; (2) Max Falkenberg, City University of London, Department of Mathematics, London EC1V 0HB, (UK), this author contributed equally, and Corresponding authors: max.falkenberg@city.ac.uk; (3) Andrea Baronchelli, City University of London, Department of Mathematics, London EC1V 0HB, (UK), The Alan Turing Institute, British Library, London NW1 2DB, (UK), and Corresponding authors: abaronchelli@turing.ac.uk. Authors: Authors: (1) Amin Mekacher, City University of London, Department of Mathematics, London EC1V 0HB, (UK) and this author contributed equally; (2) Max Falkenberg, City University of London, Department of Mathematics, London EC1V 0HB, (UK), this author contributed equally, and Corresponding authors: max.falkenberg@city.ac.uk; (3) Andrea Baronchelli, City University of London, Department of Mathematics, London EC1V 0HB, (UK), The Alan Turing Institute, British Library, London NW1 2DB, (UK), and Corresponding authors: abaronchelli@turing.ac.uk. Table of Links Abstract and Introduction Abstract and Introduction Results Results Discussion & Conclusion Discussion & Conclusion Methods Methods Acknowledgements, Data availability, and References Acknowledgements, Data availability, and References METHODS Gettr data The data used for this study has been collected using GoGettr [76], a public client developed by the Stanford Internet Observatory to give access to the Gettr API. This API allows to query user interactions, including the posts they like or share. User profiles were initially collected through a snowball sampling, by using highly popular accounts on the platform as seed users, and using the API to query their follower and following list, before repeating the same process for a random sample of the newly retrieved users. Repeating this process many times ensures that our dataset is near-complete for the studied time period. To attract more users from Twitter, Gettr previously offered a feature that would automatically import a user’s tweets upon creating an account. However, due to Twitter blocking this capability on July 10, 2021[77], Gettr had to discontinue this feature. To ensure the accuracy of our results, any posts imported before July 10, 2021, and any Gettr post whose timestamp precedes the account’s creation date were removed from our dataset. To ensure our case study on the Brazilian rightwing group encompasses the Bras´ılia insurrection, we expanded the data collection time frame for any user associated with the Brazilian community. The data collection was run in July 2022 for every user whose profile we have retrieved, and in January 2023 for the users in the Brazilian cohort. Twitter data For each verified Gettr account where the Gettr API references their Twitter followers in the account metadata, we check that the Twitter account with the same username is active using the Twitter API (see https://developer.twitter.com/en/ products/twitter-api/academic-research). Then, for each active account we download their Twitter timeline including all tweets and retweets in the period July 2021 to May 2022. This totals approximately 12 million tweets. Data was collected between September and October 2022, preceding Elon Musk’s amnesty of suspended Twitter accounts. User labelling Throughout our analysis, we label verified Gettr users as being either “matched” or “banned”, depending on whether their corresponding Twitter account is active or suspended. Any verified user who decided to link their Twitter account on their Gettr page has their Twitter follower count displayed on their profile, which can also be retrieved from the Gettr API. We stress that this selfdeclaration permits cross-platform matching since users can “reasonably expect” that their Gettr accounts will be associated with their Twitter accounts. To match accounts across platforms, we assumed that users picked the same username on both Gettr and Twitter, and we used the Twitter API to retrieve their Twitter activity. A user is classified as “banned” if the Twitter user endpoint returned an error indicating that the account has been suspended. Note that for data privacy reasons, all analysis of users across platforms is aggregated at the cohort level; we do not present results for individual users. Calculating account survival The Kaplan-Meier estimate is a tool used to quantify the survival rate of a population (in our case, users active on a social platform) over time. For each time step t, we measure how many users become indefinitely inactive, and we quantify the survival rate as Topic Modelling Gettr and Twitter content is analysed using the BERTopic topic modelling library [32]. This method extracts latent topics from an ensemble of documents (in our case Gettr posts and Twitter tweets). The base model uses pre-trained transformer-based language models to construct document embeddings which are then clustered. These methods are known to struggle with very short documents which are common on micro-blogging sites. Hence, we train our topic model using exclusively Gettr posts which are longer than 100 characters. To avoid any single user dominating a specific topic, we limit the training set to no more than 50 posts from any given user. Latent ideology To calculate the latent ideology on Gettr, we use the method developed in [36, 37] and filtering procedures from [38]. The method uses a bipartite approach where it classifies Gettr accounts as influencers or regular users. It then generates an ordering of users based on the interaction patterns of regular users with the influencer set. In the current paper, we select the matched and banned user sets as our influencers, and the remaining set of Gettr users as our cohort of regular users. Two factors can conflate the latent ideology: (1) account geography, and (2) a lack of user-influencer interactions. Since we are interested in the segregation of the banned and matched cohorts based on political ideology, the former is problematic because country-specific communities on social media can appear structurally segregated from a related community in other countries, even if they are politically aligned. For this reason, we remove a small number of accounts associated with the UK and China from our set of Gettr influencers. In the latter case, a lack of user-influencer interactions can be problematic since influencers with few user interactions appear as erroneous outliers when computing the latent ideology, often because they do not take active part in the conversation. Hence, we restrict our influencer set to the 500 banned and matched accounts who receive the largest number of interactions from other users on Gettr. In the current study, we consider any interaction type including comments, shares and likes. The latent ideology is robust as long as the number of influencers used is larger than 200 accounts [38]. In order to assess the modality of the ideology distributions, we use Hartigan’s diptest. This approach is used to identify polarized social media conversations and echo-chambers [37, 38]. Hartigan’s diptest compares a test distribution against a matched unimodal distribution to assess distribution modality [39]. The test computes the distance, D, between the cumulative density of the test distribution and the cumulative density of the matched unimodal distribution. The D-statistic is accompanied by a p-value which quantifies whether the test distribution is significantly different to a matched unimodal distribution. A p-value of less than 0.01 indicates a multimodal distribution. Toxicity analysis The toxicity of Gettr and Twitter content is computed using the Google Perspective API [42], which has been used in several social media studies to assess platform toxicity [78–80]. Given a text input, the API returns a score between 0 and 1, indicating how likely a human moderator is to flag the text as being toxic. For our analysis, we used the flagship attribute “toxicity”, which is defined as “[a] rude, disrespectful, or unreasonable comment that is likely to make people leave a discussion” [81]. When computing statistics for the toxicity of each user cohort, we apply a bootstrapping procedure to avoid erroneous results from variable cohort sizes. This is important since the distribution of post toxicity is fat tailed; there are far more posts with low toxicity than high toxicity. Therefore, a smaller set of posts from a user cohort may have a lower median toxicity, purely due to sampling effects. To avoid this conflation, bootstrapping is employed where equally sized samples are drawn from each cohort (usually 100 posts), and the median toxicity is computed for each sample. Then, the sampling procedure is repeated 100 times to compute the median and inter-quartile range for the sampled post toxicity. News media classification using Media Bias / Fact Check For the quote-ratio analysis in Fig. 5, we identify the Twitter handles of news media outlets and classify their political leaning using Media Bias / Fact Check (MBFC; see https://mediabiasfactcheck.com/). Ratings provided by MBFC are largely similar to other reputable media rating datasets [60]. MBFC classify news outlets under seven leaning categories: extreme left, left, center-left, least (media considered unbiased), center-right, right, extreme right. To ensure that we have enough news media outlets to enable the quote-ratio analysis, we group these classifications into four larger groups: Left – (extreme left, left, center-left), Least – (least), Right – (center-right, right), Far-right – (extreme right). Note that we have chosen to use the terminology “far-right” instead of “extremeright” since the former is more common in the academic literature. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

The Systemic Impact of Deplatforming on Social Media: Methods

Authors:
(1) Amin Mekacher, City University of London, Department of Mathematics, London EC1V 0HB, (UK) and this author contributed equally;
(2) Max Falkenberg, City University of London, Department of Mathematics, London EC1V 0HB, (UK), this author contributed equally, and Corresponding authors: max.falkenberg@city.ac.uk;
(3) Andrea Baronchelli, City University of London, Department of Mathematics, London EC1V 0HB, (UK), The Alan Turing Institute, British Library, London NW1 2DB, (UK), and Corresponding authors: abaronchelli@turing.ac.uk. Table of Links Abstract and Introduction Results Discussion & Conclusion Methods Acknowledgements, Data availability, and References DISCUSSION & CONCLUSION In this paper, we have analysed self-declared user-level matched data to study the ban-induced platform migration from Twitter to Gettr. First, we showed that the banned cohort of users deplatformed from Twitter are more active on Gettr, and have higher platform retention, than the matched cohort who remain active on Twitter. Second, we revealed that Gettr content primarily discusses themes relevant to the US political right. Topics overepresented on Gettr are known to have resulted in account suspensions on Twitter. Third, we showed that the matched and banned cohorts share the same politically homogeneous Gettr audience. Fourth, we found that matched users are more toxic on Twitter than they are on Gettr, and that these toxic tweets often directly mention political opponents. Finally, we highlighted Gettr’s broader societal impact, revealing significant structural changes in the Gettr interaction network in the run-up to the Bras´ılia insurrections. The fact that the banned and matched cohorts appear similar in every regard, apart from their activity and retention on Gettr, is evidence of the systemic impact of deplatforming. Fringe platforms offer a safe haven where deplatformed users are free to capitalise on their supporters following suspension from Twitter. However, in this politically homogeneous environment, users are essentially confined to an ideological “echo-platform” [43, 44] where they cannot engage and confront their political opponents. Our results hint that this ability to interact with opponents may be part of Twitter’s appeal for farright social media users, although more work is needed to fully clarify this observation. When users are banned from mainstream platforms, they become wholly dependent on fringe alternatives. This poses a societal risk since fringe platforms can facilitate the emergence of radical narratives and the spread of hate speech [9, 20, 70–72]. A lack of monitoring can, therefore, mean that signs of collective upheaval are missed. The Bras´ılia insurrection is a clear example of this. Critically, these considerations are not only relevant for mainstream platforms, whose policies have received the most scrutiny, but for the whole social media ecosystem. Despite branding itself as a free speech platform, Gettr has also deplatformed users for posting radical content, most notably the white supremacist Nicholas Fuentes [73]. It is important to contextualise the scope of our findings, whose limitations are avenues for future work. First, the current study only considers the migration from Twitter to Gettr, since users of other platforms do not declare their Twitter use as standard. If data becomes available, future work should extend scrutiny to other platforms. Second, the Gettr matching feature only applies to verified users, a subset of the users who migrated from Twitter to Gettr. Analysing non-verified users migrating across platforms would clarify the differential impact of deplatforming on content creators as opposed to consumers. Finally, we cannot study tweets from the banned cohort since this data is not publicly available. Analysing this content would explain why some users are deplatformed, but others are not. Overall, our study highlights how Gettr struggles to compete with Twitter when users have free choice to use either platform. However, the decision by Twitter to deplatform a user impacts how those users view Gettr as an alternative. We anticipate that future work will build on these observations and speculate that other fringe platforms will likely show a similar dependence on their mainstream competitors. This work is urgently needed given the risks posed to democracy by poorly regulated social media [74, 75]. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Amin Mekacher, City University of London, Department of Mathematics, London EC1V 0HB, (UK) and this author contributed equally; (2) Max Falkenberg, City University of London, Department of Mathematics, London EC1V 0HB, (UK), this author contributed equally, and Corresponding authors: max.falkenberg@city.ac.uk; (3) Andrea Baronchelli, City University of London, Department of Mathematics, London EC1V 0HB, (UK), The Alan Turing Institute, British Library, London NW1 2DB, (UK), and Corresponding authors: abaronchelli@turing.ac.uk. Authors: Authors: (1) Amin Mekacher, City University of London, Department of Mathematics, London EC1V 0HB, (UK) and this author contributed equally; (2) Max Falkenberg, City University of London, Department of Mathematics, London EC1V 0HB, (UK), this author contributed equally, and Corresponding authors: max.falkenberg@city.ac.uk; (3) Andrea Baronchelli, City University of London, Department of Mathematics, London EC1V 0HB, (UK), The Alan Turing Institute, British Library, London NW1 2DB, (UK), and Corresponding authors: abaronchelli@turing.ac.uk. Table of Links Abstract and Introduction Abstract and Introduction Results Results Discussion & Conclusion Discussion & Conclusion Methods Methods Acknowledgements, Data availability, and References Acknowledgements, Data availability, and References DISCUSSION & CONCLUSION In this paper, we have analysed self-declared user-level matched data to study the ban-induced platform migration from Twitter to Gettr. First, we showed that the banned cohort of users deplatformed from Twitter are more active on Gettr, and have higher platform retention, than the matched cohort who remain active on Twitter. Second, we revealed that Gettr content primarily discusses themes relevant to the US political right. Topics overepresented on Gettr are known to have resulted in account suspensions on Twitter. Third, we showed that the matched and banned cohorts share the same politically homogeneous Gettr audience. Fourth, we found that matched users are more toxic on Twitter than they are on Gettr, and that these toxic tweets often directly mention political opponents. Finally, we highlighted Gettr’s broader societal impact, revealing significant structural changes in the Gettr interaction network in the run-up to the Bras´ılia insurrections. The fact that the banned and matched cohorts appear similar in every regard, apart from their activity and retention on Gettr, is evidence of the systemic impact of deplatforming. Fringe platforms offer a safe haven where deplatformed users are free to capitalise on their supporters following suspension from Twitter. However, in this politically homogeneous environment, users are essentially confined to an ideological “echo-platform” [43, 44] where they cannot engage and confront their political opponents. Our results hint that this ability to interact with opponents may be part of Twitter’s appeal for farright social media users, although more work is needed to fully clarify this observation. When users are banned from mainstream platforms, they become wholly dependent on fringe alternatives. This poses a societal risk since fringe platforms can facilitate the emergence of radical narratives and the spread of hate speech [9, 20, 70–72]. A lack of monitoring can, therefore, mean that signs of collective upheaval are missed. The Bras´ılia insurrection is a clear example of this. Critically, these considerations are not only relevant for mainstream platforms, whose policies have received the most scrutiny, but for the whole social media ecosystem. Despite branding itself as a free speech platform, Gettr has also deplatformed users for posting radical content, most notably the white supremacist Nicholas Fuentes [73]. It is important to contextualise the scope of our findings, whose limitations are avenues for future work. First, the current study only considers the migration from Twitter to Gettr, since users of other platforms do not declare their Twitter use as standard. If data becomes available, future work should extend scrutiny to other platforms. Second, the Gettr matching feature only applies to verified users, a subset of the users who migrated from Twitter to Gettr. Analysing non-verified users migrating across platforms would clarify the differential impact of deplatforming on content creators as opposed to consumers. Finally, we cannot study tweets from the banned cohort since this data is not publicly available. Analysing this content would explain why some users are deplatformed, but others are not. Overall, our study highlights how Gettr struggles to compete with Twitter when users have free choice to use either platform. However, the decision by Twitter to deplatform a user impacts how those users view Gettr as an alternative. We anticipate that future work will build on these observations and speculate that other fringe platforms will likely show a similar dependence on their mainstream competitors. This work is urgently needed given the risks posed to democracy by poorly regulated social media [74, 75]. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

The Systemic Impact of Deplatforming on Social Media: Discussion & Conclusion

The Systemic Impact of Deplatforming on Social Media: Results

Authors:
(1) Amin Mekacher, City University of London, Department of Mathematics, London EC1V 0HB, (UK) and this author contributed equally;
(2) Max Falkenberg, City University of London, Department of Mathematics, London EC1V 0HB, (UK), this author contributed equally, and Corresponding authors: max.falkenberg@city.ac.uk;
(3) Andrea Baronchelli, City University of London, Department of Mathematics, London EC1V 0HB, (UK), The Alan Turing Institute, British Library, London NW1 2DB, (UK), and Corresponding authors: abaronchelli@turing.ac.uk. Table of Links Abstract and Introduction Results Discussion & Conclusion Methods Acknowledgements, Data availability, and References Deplatforming, or banning malicious accounts from social media, is a key tool for moderating online harms. However, the consequences of deplatforming for the wider social media ecosystem have been largely overlooked so far, due to the difficulty of tracking banned users. Here, we address this gap by studying the ban-induced platform migration from Twitter to Gettr. With a matched dataset of 15M Gettr posts and 12M Twitter tweets, we show that users active on both platforms post similar content as users active on Gettr but banned from Twitter, but the latter have higher retention and are 5 times more active. Then, we reveal that matched users are more toxic on Twitter, where they can engage in abusive cross-ideological interactions, than Gettr. Our analysis shows that the matched cohort are ideologically aligned with the farright, and that the ability to interact with political opponents may be part of the appeal of Twitter to these users. Finally, we identify structural changes in the Gettr network preceding the 2023 Bras´ılia insurrections, highlighting how deplatforming from mainstream social media can fuel poorly-regulated alternatives that may pose a risk to democratic life. Social media has always been controversial, with constant debate around which content should be permitted, which content should be banned, and the conditions under which a user should be deplatformed for breaking the rules [1, 2]. Particularly since the US Capitol insurrections, the deplatforming question has become a cornerstone of the polarized public discourse, with major social media companies facing increased pressure to deplatform malicious users [3]. Removing malicious accounts from social media helps protect other users and limits the spread of content which has the potential to cause harm [4, 5]. The scientific literature supports this view showing that many harmful communities are no longer active on mainstream platforms; these groups previously thrived by posting hate speech or conspiracy theories [4, 6–17], with their dense interaction networks facilitating a broad reach for their content [18]. However, the benefits of deplatforming for users on a mainstream platform do not account for the impact of these moderation policies on the wider social media ecosystem. Specifically, it remains unclear how banning accounts from one platform may drive migrations to weakly regulated and poorly monitored fringe alternatives where violent narratives may develop and thrive [9, 19–24]. This is in large part because data is rarely available which permits the cross-platform tracking of social media users, particularly following account suspensions. sions. In this paper, we present a unique dataset which addresses this gap, focusing on a matched cohort of users who migrated from Twitter to Gettr, a Twitter-clone that has attracted many of Twitter’s most high-profile suspended accounts including US congresswoman Marjorie Taylor-Greene, media executive Steve Bannon, and conspiracy theorist Alex Jones. Our dataset presents the near-complete evolution of Gettr from its founding in July 2021 to May 2022 including 15M posts from 785,360 active users who have posted at least once. Of these users, 6,152 are verified, 1,588 of which self-declare as active on Twitter (see Methods). For these 1,588 self-declared Twitter users with a verified Gettr account, we download their Twitter timeline from July 2021 to May 2022 totalling 12M tweets and retweets. These users represent the “matched” cohort, with analysis of their Gettr posts (Twitter tweets) referred to as “matched Gettr” (“matched Twitter”) below. For the remaining verified Gettr users, we use the Twitter API to identify those accounts which have been suspended from the platform, assuming accounts share the same username on both platforms, totalling 454 accounts who constitute the “banned” cohort. Finally, all remaining users who are not verified on Gettr are part of the “non-verified” cohort. In the remainder of the paper, we will overview account activity and retention on Gettr, showing that the banned cohort are 5 times more active than the matched cohort. Despite this, our results will show that these two cohorts are structurally mixed on Gettr, sharing the same politically homogenous audience and posting similar content. Using matched cohort tweets, we will show that Gettr is generally representative of the US far-right, and that matched users are more toxic on Twitter than they are on Gettr. Finally, we will highlight how Gettr had a global impact, outlining the structural changes in the Portuguese-language Gettr network that emerged in the run up to the January 2023 riots in Brazil. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Amin Mekacher, City University of London, Department of Mathematics, London EC1V 0HB, (UK) and this author contributed equally; (2) Max Falkenberg, City University of London, Department of Mathematics, London EC1V 0HB, (UK), this author contributed equally, and Corresponding authors: max.falkenberg@city.ac.uk; (3) Andrea Baronchelli, City University of London, Department of Mathematics, London EC1V 0HB, (UK), The Alan Turing Institute, British Library, London NW1 2DB, (UK), and Corresponding authors: abaronchelli@turing.ac.uk. Authors: Authors: (1) Amin Mekacher, City University of London, Department of Mathematics, London EC1V 0HB, (UK) and this author contributed equally; (2) Max Falkenberg, City University of London, Department of Mathematics, London EC1V 0HB, (UK), this author contributed equally, and Corresponding authors: max.falkenberg@city.ac.uk; (3) Andrea Baronchelli, City University of London, Department of Mathematics, London EC1V 0HB, (UK), The Alan Turing Institute, British Library, London NW1 2DB, (UK), and Corresponding authors: abaronchelli@turing.ac.uk. Table of Links Abstract and Introduction Abstract and Introduction Results Results Discussion & Conclusion Discussion & Conclusion Methods Methods Acknowledgements, Data availability, and References Acknowledgements, Data availability, and References Deplatforming, or banning malicious accounts from social media, is a key tool for moderating online harms. However, the consequences of deplatforming for the wider social media ecosystem have been largely overlooked so far, due to the difficulty of tracking banned users. Here, we address this gap by studying the ban-induced platform migration from Twitter to Gettr. With a matched dataset of 15M Gettr posts and 12M Twitter tweets, we show that users active on both platforms post similar content as users active on Gettr but banned from Twitter, but the latter have higher retention and are 5 times more active. Then, we reveal that matched users are more toxic on Twitter, where they can engage in abusive cross-ideological interactions, than Gettr. Our analysis shows that the matched cohort are ideologically aligned with the farright, and that the ability to interact with political opponents may be part of the appeal of Twitter to these users. Finally, we identify structural changes in the Gettr network preceding the 2023 Bras´ılia insurrections, highlighting how deplatforming from mainstream social media can fuel poorly-regulated alternatives that may pose a risk to democratic life. Deplatforming, or banning malicious accounts from social media, is a key tool for moderating online harms. However, the consequences of deplatforming for the wider social media ecosystem have been largely overlooked so far, due to the difficulty of tracking banned users. Here, we address this gap by studying the ban-induced platform migration from Twitter to Gettr. With a matched dataset of 15M Gettr posts and 12M Twitter tweets, we show that users active on both platforms post similar content as users active on Gettr but banned from Twitter, but the latter have higher retention and are 5 times more active. Then, we reveal that matched users are more toxic on Twitter, where they can engage in abusive cross-ideological interactions, than Gettr. Our analysis shows that the matched cohort are ideologically aligned with the farright, and that the ability to interact with political opponents may be part of the appeal of Twitter to these users. Finally, we identify structural changes in the Gettr network preceding the 2023 Bras´ılia insurrections, highlighting how deplatforming from mainstream social media can fuel poorly-regulated alternatives that may pose a risk to democratic life. Social media has always been controversial, with constant debate around which content should be permitted, which content should be banned, and the conditions under which a user should be deplatformed for breaking the rules [1, 2]. Particularly since the US Capitol insurrections, the deplatforming question has become a cornerstone of the polarized public discourse, with major social media companies facing increased pressure to deplatform malicious users [3]. Removing malicious accounts from social media helps protect other users and limits the spread of content which has the potential to cause harm [4, 5]. The scientific literature supports this view showing that many harmful communities are no longer active on mainstream platforms; these groups previously thrived by posting hate speech or conspiracy theories [4, 6–17], with their dense interaction networks facilitating a broad reach for their content [18]. However, the benefits of deplatforming for users on a mainstream platform do not account for the impact of these moderation policies on the wider social media ecosystem. Specifically, it remains unclear how banning accounts from one platform may drive migrations to weakly regulated and poorly monitored fringe alternatives where violent narratives may develop and thrive [9, 19–24]. This is in large part because data is rarely available which permits the cross-platform tracking of social media users, particularly following account suspensions. sions. In this paper, we present a unique dataset which addresses this gap, focusing on a matched cohort of users who migrated from Twitter to Gettr, a Twitter-clone that has attracted many of Twitter’s most high-profile suspended accounts including US congresswoman Marjorie Taylor-Greene, media executive Steve Bannon, and conspiracy theorist Alex Jones. Our dataset presents the near-complete evolution of Gettr from its founding in July 2021 to May 2022 including 15M posts from 785,360 active users who have posted at least once. Of these users, 6,152 are verified, 1,588 of which self-declare as active on Twitter (see Methods). For these 1,588 self-declared Twitter users with a verified Gettr account, we download their Twitter timeline from July 2021 to May 2022 totalling 12M tweets and retweets. These users represent the “matched” cohort, with analysis of their Gettr posts (Twitter tweets) referred to as “matched Gettr” (“matched Twitter”) below. For the remaining verified Gettr users, we use the Twitter API to identify those accounts which have been suspended from the platform, assuming accounts share the same username on both platforms, totalling 454 accounts who constitute the “banned” cohort. Finally, all remaining users who are not verified on Gettr are part of the “non-verified” cohort. In the remainder of the paper, we will overview account activity and retention on Gettr, showing that the banned cohort are 5 times more active than the matched cohort. Despite this, our results will show that these two cohorts are structurally mixed on Gettr, sharing the same politically homogenous audience and posting similar content. Using matched cohort tweets, we will show that Gettr is generally representative of the US far-right, and that matched users are more toxic on Twitter than they are on Gettr. Finally, we will highlight how Gettr had a global impact, outlining the structural changes in the Portuguese-language Gettr network that emerged in the run up to the January 2023 riots in Brazil. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

The Systemic Impact of Deplatforming on Social Media: Abstract and Introduction

Double Trouble: The Curse of Redundant Structures TL;DR: Parallel hierarchies lead to duplication and tight coupling. Problems Increased complexity
DRY / Code Duplication
Maintenance Nightmare
Coupling
Ripple Effect
Potential for inconsistencies across different hierarchies Solutions Merge hierarchies
Use composition
Extract Common Functionality Refactorings https://hackernoon.com/refactoring-013-eliminating-repeated-code-with-dry-principles?embedable=true https://maximilianocontieri.com/refactoring-007-extract-class?embedable=true Context Parallel hierarchies occur when you must make a counterpart every time you create a domain class. The counterpart might be persistence, UI, Controller, tests, Serialization, etc. This leads to duplicate structures and tight coupling. Changes in the domain model require changes in the parallel classes, making the system more brittle and harder to manage. Sample Code Wrong // Domain classes
abstract class Transaction {
    private String id;
    private double amount;
}

class BankTransaction extends Transaction {
    private String bankName;
}

class CreditCardTransaction extends Transaction {
    private String cardNumber;
}

// Persistence classes
abstract class TransactionDAO {
    private String id;
    private double amount;
}

class BankTransactionDAO extends TransactionDAO {
    private String bankName;
}

class CreditCardTransactionDAO extends TransactionDAO {
    private String cardNumber;
} Right public class TransactionService {
    private EntityManager entityManager;

    public TransactionService(EntityManager entityManager) {
        this.entityManager = entityManager;
    }

    public void saveTransaction(Transaction transaction) {
        entityManager.getTransaction().begin();
        entityManager.persist(transaction);
        entityManager.getTransaction().commit();
    }

    public Transaction loadTransaction(
        Long id, Class<? extends Transaction> transactionClass) {
        return entityManager.find(transactionClass, id);
    }
} Detection [x]Semi-Automatic You can detect this smell by traversing the hierarchies Exceptions Some frameworks force you to extend your domain using this technique Tags Hierarchies Level [x]Intermediate AI Generation AI generators often create this smell by mirroring domain models in persistence layers without understanding the implications, leading to unnecessary duplication. AI Detection AI Assistants can fix this smell with instructions to consolidate hierarchies and use composition, reducing duplication and improving maintainability. ChatGPT offered a solution using 'Instanceof' which is an even worse code smell Conclusion Parallel hierarchies create unnecessary complexity and make the codebase harder to maintain. They bring deep hierarchies, which is a symptom of subclassification for code reuse You can merge the hierarchies and use composition to simplify the design and improve the system's robustness. You can use Metaprogramming to manage the persistence or the unit tests. Metaprogramming is also a code smell when you use it for domain problems, but persistence and testing are orthogonal domains. Relations Code Smell 137 - Inheritance Tree Too Deep Code Smell 11 - Subclassification for Code Reuse Code Smell 58 - Yo-yo Problem More Info https://hackernoon.com/laziness-chapter-i-meta-programming-6s4l300e?embedable=true Disclaimer Code Smells are my opinion. Inheritance is surely a good answer but who knows the questions? Michel Gauthier Software Engineering Great Quotes This article is part of the CodeSmell Series. How to Find the Stinky Parts of your Code Double Trouble: The Curse of Redundant Structures Double Trouble: The Curse of Redundant Structures TL;DR: Parallel hierarchies lead to duplication and tight coupling. TL;DR: Parallel hierarchies lead to duplication and tight coupling. Problems Increased complexity DRY / Code Duplication Maintenance Nightmare Coupling Ripple Effect Potential for inconsistencies across different hierarchies Increased complexity DRY / Code Duplication Maintenance Nightmare Coupling Ripple Effect Potential for inconsistencies across different hierarchies Solutions Merge hierarchies Use composition Extract Common Functionality Merge hierarchies Use composition Extract Common Functionality Refactorings https://hackernoon.com/refactoring-013-eliminating-repeated-code-with-dry-principles?embedable=true https://hackernoon.com/refactoring-013-eliminating-repeated-code-with-dry-principles?embedable=true https://maximilianocontieri.com/refactoring-007-extract-class?embedable=true https://maximilianocontieri.com/refactoring-007-extract-class?embedable=true Context Parallel hierarchies occur when you must make a counterpart every time you create a domain class. The counterpart might be persistence, UI, Controller, tests, Serialization, etc. This leads to duplicate structures and tight coupling. Changes in the domain model require changes in the parallel classes, making the system more brittle and harder to manage. Sample Code Wrong // Domain classes
abstract class Transaction {
    private String id;
    private double amount;
}

class BankTransaction extends Transaction {
    private String bankName;
}

class CreditCardTransaction extends Transaction {
    private String cardNumber;
}

// Persistence classes
abstract class TransactionDAO {
    private String id;
    private double amount;
}

class BankTransactionDAO extends TransactionDAO {
    private String bankName;
}

class CreditCardTransactionDAO extends TransactionDAO {
    private String cardNumber;
} // Domain classes
abstract class Transaction {
    private String id;
    private double amount;
}

class BankTransaction extends Transaction {
    private String bankName;
}

class CreditCardTransaction extends Transaction {
    private String cardNumber;
}

// Persistence classes
abstract class TransactionDAO {
    private String id;
    private double amount;
}

class BankTransactionDAO extends TransactionDAO {
    private String bankName;
}

class CreditCardTransactionDAO extends TransactionDAO {
    private String cardNumber;
} Right public class TransactionService {
    private EntityManager entityManager;

    public TransactionService(EntityManager entityManager) {
        this.entityManager = entityManager;
    }

    public void saveTransaction(Transaction transaction) {
        entityManager.getTransaction().begin();
        entityManager.persist(transaction);
        entityManager.getTransaction().commit();
    }

    public Transaction loadTransaction(
        Long id, Class<? extends Transaction> transactionClass) {
        return entityManager.find(transactionClass, id);
    }
} public class TransactionService {
    private EntityManager entityManager;

    public TransactionService(EntityManager entityManager) {
        this.entityManager = entityManager;
    }

    public void saveTransaction(Transaction transaction) {
        entityManager.getTransaction().begin();
        entityManager.persist(transaction);
        entityManager.getTransaction().commit();
    }

    public Transaction loadTransaction(
        Long id, Class<? extends Transaction> transactionClass) {
        return entityManager.find(transactionClass, id);
    }
} Detection [x]Semi-Automatic [x] Semi-Automatic [x] You can detect this smell by traversing the hierarchies Exceptions Some frameworks force you to extend your domain using this technique Some frameworks force you to extend your domain using this technique Tags Hierarchies Hierarchies Level [x]Intermediate [x] Intermediate [x] AI Generation AI generators often create this smell by mirroring domain models in persistence layers without understanding the implications, leading to unnecessary duplication. AI Detection AI Assistants can fix this smell with instructions to consolidate hierarchies and use composition, reducing duplication and improving maintainability. ChatGPT offered a solution using 'Instanceof' which is an even worse code smell 'Instanceof' Conclusion Parallel hierarchies create unnecessary complexity and make the codebase harder to maintain. They bring deep hierarchies , which is a symptom of subclassification for code reuse deep hierarchies subclassification for code reuse You can merge the hierarchies and use composition to simplify the design and improve the system's robustness. You can use Metaprogramming to manage the persistence or the unit tests. Metaprogramming is also a code smell when you use it for domain problems, but persistence and testing are orthogonal domains. Metaprogramming Relations Code Smell 137 - Inheritance Tree Too Deep Code Smell 137 - Inheritance Tree Too Deep Code Smell 11 - Subclassification for Code Reuse Code Smell 11 - Subclassification for Code Reuse Code Smell 58 - Yo-yo Problem Code Smell 58 - Yo-yo Problem More Info https://hackernoon.com/laziness-chapter-i-meta-programming-6s4l300e?embedable=true https://hackernoon.com/laziness-chapter-i-meta-programming-6s4l300e?embedable=true Disclaimer Code Smells are my opinion . opinion Inheritance is surely a good answer but who knows the questions? Inheritance is surely a good answer but who knows the questions? Michel Gauthier Michel Gauthier Software Engineering Great Quotes Software Engineering Great Quotes This article is part of the CodeSmell Series. How to Find the Stinky Parts of your Code How to Find the Stinky Parts of your Code

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