{"id":87991,"date":"2025-10-30T14:27:16","date_gmt":"2025-10-30T14:27:16","guid":{"rendered":"https:\/\/proxidize.com\/?post_type=blog&p=87991"},"modified":"2026-01-13T17:26:42","modified_gmt":"2026-01-13T17:26:42","slug":"twitter-scraper","status":"publish","type":"blog","link":"https:\/\/proxidize.com\/blog\/twitter-scraper\/","title":{"rendered":"Twitter Scraper: How to Scrape Twitter for Free"},"content":{"rendered":"\n

Let\u2019s say you want to know people’s opinions on a specific topic, maybe you want to do sentiment analysis for them for research purposes, or you\u2019re a software engineer who has been tasked with scraping (this is me). There\u2019s no better place to do it than Twitter\/X. Millions of people use it every day to tweet and talk about every possible topic under the sun. However in order to accomplish this at any meaningful scale (or efficiency), you must scrape, i.e. use code that collects data for you while you drink coffee or browse the internet.<\/p>\n\n\n\n

If you’ve got a tech background, you’ll likely be familiar with web scraping. If not, we’ll walk you through it. This article will be a step-by-step breakdown of how I built an open-source Twitter scraper that allows you to scrape all (or some) of an X account’s posts. Too many guides leave you with just that, so we went a step further and have included an AI integration for analysis of the tweets you’ve scraped \u2014 something that might appeal to everyone from researchers and academics to OSINT Twitter.<\/p>\n\n\n\n

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High-quality scraping and automation \nstarts with high-quality mobile proxies<\/p>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tBuy Proxies Now<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\n\n\n\n

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Despite having far fewer monthly active users (611 million compared to Facebook\u2019s 3 billion), Twitter statistics<\/a> show that the average Twitter\/X user engages with the site for longer and more deeply than other social media sites. The rules are completely different, with many people using Twitter at work, using it to follow real-time news and events, and more.<\/p>\n\n\n\n

As a platform, Twitter\/X is one of the toughest ones to scrape. It has everything from  anti-bot detection, captchas, and IP bans, all to prevent people from scraping, but with proxies<\/a> and IP rotation, everything is possible.<\/p>\n\n\n\n

Together we\u2019ll go on the step-by-step journey of how I suffered to deliver this amazing code. We\u2019ll include what technology stack I used, why cookies are so important and how they can be used properly. I\u2019ll also explain how I figured out Twitter\/X\u2019s infinite scrolling, when to stop scrolling, and how to pick up where you left off across multiple sessions. If you\u2019re not interested in the journey and just want the repo, here you go<\/a><\/em>.<\/strong> We also have a Reddit scraper<\/a> you might be interested in.<\/p>\n\n\n\n

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Twitter Scraping: The Technology Stack (It\u2019s Not Just About Speed)<\/h2>\n\n\n\n

For this project I needed to choose an ecosystem that fit the needs of scraping a platform as strict as Twitter\/X. My first choice was to go for speed over quality but that was a big mistake, it turned out. I went with Python and Selenium<\/a> first, but the results were not good in terms of accuracy of the data. I mixed things up and switched to using Python with Playwright, which offered a great mix of speed and accuracy to the project.<\/p>\n\n\n\n

Python Twitter Scraper: The Obvious Choice <\/h3>\n\n\n\n

Python<\/a> dominates web scraping. If you have seen a web scraping<\/a> tutorial, a data pipeline, or an automation script, the chances it\u2019s written in Python will be high.<\/p>\n\n\n\n

Python isn\u2019t just<\/em> popular because it\u2019s \u201ceasy to learn\u201d (though it is). It\u2019s popular because it solves the entire workflow. Python libraries for webscraping<\/a>, parsing, processing, analysing, exporting \u2014 there are mature libraries for every single step. You don\u2019t have to switch languages halfway through your project; it\u2019s Python from start to finish.<\/p>\n\n\n\n

Python\u2019s syntax reads like English, which means less time spent debugging errors (I hope) and more time building. What\u2019s Python\u2019s real power? Its ecosystem.<\/p>\n\n\n\n

Selenium, Playwright, BeautifulSoup<\/a>, Scrapy every major scraping tool has Python support. The community is everywhere, and when you hit a problem, someone has already solved it on Stack Overflow or you can just use AI to help you.<\/p>\n\n\n\n

Here\u2019s the part people miss: Python isn’t just good at scraping, it\u2019s good at everything that comes after scraping. Some developers say “Python is only for scraping,\u201d and they\u2019re wrong. Python excels at data processing, data analysis, AI integration, and automation. <\/p>\n\n\n\n

What that means for our Twitter\/X scraper is that we can:<\/p>\n\n\n\n