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Seeking Proven Workarounds for Twitter Data Scraping Challenges Post API V2 Update

I'm currently grappling with Twitter's API v2 updates, which have significantly impacted the effectiveness of data scraping libraries like Tweepy and Scrapy. 

The new rate limits and access restrictions have introduced a set of challenges that I'm trying to navigate.

Specifically, I'm encountering problems with:

Adhering to the new rate limits without compromising the continuity of data collection.

Gaining access to certain data types that seem to have new restrictions.

Ensuring that my data scraping remains compliant with Twitter's updated terms of service.

Here are the steps I've already taken:

  • I've updated the libraries to the latest versions, hoping to align with the new API requirements.

    I've scoured the Twitter API documentation to understand the updated restrictions and modify my code accordingly.

    I've researched alternative scraping methods and tools designed for Twitter API v2, but with limited success.

With these changes, I'm seeking guidance on the following:

  • What strategies have you found effective for scraping data within the constraints of Twitter API v2?

    Are there any updated tools or libraries that have helped you overcome these scraping hurdles?

    How do you balance effective data scraping with compliance to Twitter's revised policies?

I would greatly appreciate any insights, including code snippets, tool recommendations, or anecdotes of your experiences that could guide me through these challenges.

Thank you for your valuable input!



source https://stackoverflow.com/questions/77447591/seeking-proven-workarounds-for-twitter-data-scraping-challenges-post-api-v2-upda
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