Twitter scrapping is the process of importing data from Twitter handles and saving it in local files for analysis, allowing businesses and social scientists to explore how individuals, groups, and communities behave towards specific topics. This can be done using coding or application programming interfaces (APIs) to crawl and obtain tweet information. Automated web scraping tools have made it possible for people without prior knowledge of coding to scrape data from websites, including Twitter, by simply copying and pasting the URL of a target Twitter handle and extracting tweets using inbuilt bots that create pagination loops. Coding is also an option, requiring foreknowledge in programming languages such as Python and R, and can be used to download and install required software modules, set parameters for scrapping, and extract specific data fields from tweets. Twitter scraping has several benefits, including informing businesses about current trends, helping organizations understand customer reviews and feedback, enabling competitor analysis, enhancing influencer marketing, and boosting marketing and discovering new clients. Various tools are available for Twitter scraping, such as ScrapeStorm, Apift Twitter Profile Scraper, Octoparse, and Webscraper.io Extension, each with its own features and pricing plans, including a 14-day trial window for some of them. The application of scrapers can be used to conduct research on competitors, compare prices, and boost marketing efforts by identifying user-profiles and feeding data to shopping sites and other sellers.