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After the Micro-blogging platform Twitter faced a major outage globally on Saturday night, Twitter owner Elon Musk said that Twitter is limiting how many tweets per day various accounts can read, to discourage ‘extreme levels of data scraping and system manipulation.
According to Musk, initially, verified accounts on Twitter were allowed to read up to 6,000 posts per day, while unverified accounts had a limit of 600 posts per day, and new unverified accounts were restricted to 300. However, these reading limitations were later adjusted to 10,000 posts per day for verified users, 1,000 posts per day for unverified users, and 500 posts per day for new unverified users, as stated by Musk in a separate post without providing further details.
Rate limits increasing soon to 8000 for verified, 800 for unverified & 400 for new unverified https://t.co/fuRcJLifTn— Elon Musk (@elonmusk) July 1, 2023
The decision to require users to have a Twitter account to view tweets was announced as a temporary emergency measure by Twitter, and Musk expressed his support for this move. Musk had said that hundreds of organizations or more were scraping Twitter data “extremely aggressively”, impacting user experience.
This announcement came after Twitter was down for thousands of users on Saturday. Nearly 7,500 users across the social media platform reported issues with accessing the app during the peak of the outage at around 8:47 PM.
Earlier on Saturday, Twitter stopped browsing access on its web platform for people without accounts. It was unclear if the latest outage was a result of backend changes to perform the earlier move.
Elon Musk said it was a temporary emergency measure. “We were getting data pillaged so much that it was degrading service for normal users. Almost every company doing AI, from startups to some of the biggest corporations on Earth, was scraping vast amounts of data,” the Twitter owner said.
Musk had earlier expressed displeasure with artificial intelligence firms like OpenAI, the owner of ChatGPT, for using Twitter’s data to train their large language models.
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