Decoding Elon Musk’s Motive for Introducing Twitter View Limits: An Insight into Data Scraping
In a recent tweet, Elon Musk, Twitter’s Executive Chair, spilled the beans on their master plan to tackle the menace of excessive data scraping and system manipulation.
They’ve laid down the law, capping the number of tweets different accounts can read each day.
Initially, verified accounts were held to a maximum of 6,000 posts daily, while unverified ones had to make do with a paltry 600 posts. As for new unverified accounts, they were dealt an even harsher hand, being limited to just 300 posts per day.
Twitter View Limits Per Day
In a subsequent post, Musk unveiled the updated limits. Verified users now get a generous reading allowance of 10,000 posts per day, while unverified accounts can access up to 1,000 posts daily.
As for new unverified users, they’ve got a limit of 500 posts per day.
Though further details weren’t disclosed, it seems this change is a swift response to combat the pressing issue of aggressive data scraping and its adverse impact on user experience.
What Is Data Scrapping
Moving on, let’s take a dive into the world of data scraping. This technique, also known as web scraping, involves importing data from websites into files or spreadsheets. It’s a handy tool used to extract information from the web, either for personal use or to repurpose the data on other websites. Various software applications facilitate this automated process.
Data scraping has legitimate uses, like collecting business intelligence, determining prices for travel comparison sites, finding sales leads, and conducting market research. However, it often falls into the wrong hands and is abused for nefarious purposes, such as harvesting email addresses for spamming and scamming, or stealing copyrighted content.
Methods of Data Scraping
Now, let’s explore the methods used in data scraping. One popular technique is HTML parsing, where JavaScript is used to extract text and links from linear or nested HTML pages. Another method involves DOM parsing, which delves into the structure, style, and content of XML files, allowing scrapers to access specific nodes containing information.
Companies with ample computing power may employ vertical aggregation platforms to target specific verticals automatically, using bots that require minimal human intervention. XPath, a query language for XML documents, is often combined with DOM parsing to extract web pages effectively.
Google Sheets also comes in handy for data scraping, using the IMPORTXML function to extract specific patterns or data from websites.
Is Twitter View Limits the Answer Protect Twitter From Data Scraping?
But how can you protect your website from data scraping? Here are some strategies to consider:
Rate Limit User Requests: Set limits on the number of requests an IP address can make within a specific time frame, slowing down scraping attempts.
Mitigate High-Volume Requesters with CAPTCHAs: Introduce CAPTCHA challenges that can deter bots while allowing humans to proceed, hindering automated scraping efforts.
Regularly Modify HTML Markup: By changing HTML markup elements periodically, you can interrupt a bot’s workflow and make scraping more difficult.
Embed Content in Media Objects: Using media objects like images can thwart scraping by requiring optical character recognition (OCR) to extract data, creating an additional obstacle for bots.
Remember, while these methods can be effective, they may not guarantee complete protection against data scraping. For comprehensive safeguarding, consider deploying a robust bot protection solution that detects and blocks scraping bots before they can access your website or web application.
