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Our world is now digitized, and data has proven to be a useful component for business, entertainment, strategy formulation, and decision making. However, most researchers in diverse fields are at a loss of how to gather and collect data, format it visually, analyze it, and use it to achieve their objectives. Fortunately, web scraping is an effective method through which researchers can collect external data for use in various applications.

Web scraping is a process of collecting and analyzing large data sets to identify trends, patterns, and relationships. It is a powerful tool that researchers can incorporate in their data collection and analytics strategies. The method assists researchers to gather information from different online sources and view it in a central location. Researchers can, therefore, use web scraping as a tool for observing online communities without employing invasive methods.

The web contains a vast amount of text. The text may be trapped in PDFs, populated from diverse databases, unstructured, or organized in table formats. However, the authors and information owners structure most of the text in XHTML or HTML markup tags that instruct browsers how to display the information. They design and create tags to make text appear in a readable format on the web. Similarly, programmers and vendors develop web scraping tools to interpret the markup tags and follow the programmed instructions on how to collect necessary research data.

The essential stage in a web scraping process is to choose a tool that fits the research needs. The tools include purpose-built libraries within a popular programming language, desktop applications, and manual browser plug-ins. The capabilities and features of web scraping tools vary according to data collection needs and require different learning and time investments.

Researchers can scrape websites for information using various approaches. The most common form includes using software built to scrape vast data amounts from the web automatically. Researchers and businesses use these specialized methods and tools to automate web scraping processes by defining various metrics. They include the specific websites to visit, the information to look for and what data to scrape, and whether to collect data till the end of a page or to repeat the process in other hyperlinks recursively. Furthermore, automated web scraping methods allow researchers to define if a process should capture web data at regular intervals or in real-time to capture data changes.

Web scraping employs two main methods to extract and collect online data. These are web scrapers and web crawlers. Web crawlers use links or keywords that connect websites since they are created using artificial intelligence and programmed to find specific information. On the other hand, web scrapers consist of tools designed to extract relevant information after a web crawler identifies the required data. Researchers can also program the web scrapers to collect and sort web data in various formats to access and alter them offline. Subsequently, researchers can interact with, manipulate, or edit data using file formats, such as .JSON, .CSV, and .XLSX.

Harvesting extended sizes of data from various internet sources is a common data collection procedure for researchers. Research data typically share four main traits; the data is unstructured, it grows at an exponential rate, it is highly complicated, and it is transformational. The first stage, therefore, is identifying web scraping design depending on the research needs. Numerous free and inexpensive web scraping software tools are accessible for scraping substantial data sets. Some tools analyze the data automatically.

Researchers must then identify relevant websites for scraping data. When collecting a large set of data, researchers can define the population parameters narrowly, which is beneficial as the collected data may include almost all cases of the research phenomenon under study. Collecting a list of such instances permits researchers to use it as a sampling frame (data cases the researcher may choose to include in the research sample).

However, the main challenge in this mode of data collection is deciding the number of pages of the identified websites that should be examined for data, as indicated in the search parameters. The number of hits from a website search depends on the type of the website a researcher is scrapping. For example, large search engines may produce more pages compared to social media platforms. As such, it may be difficult for researchers since scraping data from the same number of pages irrespective of the website may lead to over-representation or under-representation of some sites.

Researchers must then decide the specific content to download from the websites. Discussion boards and blogs usually contain large quantities of discussion posts and accompanying comments, mostly in chronological order. As a result, researchers must decide the exact text they require to download. They can filter the data according to specific topics or date for more detailed information. The lasts stage is scraping the data, which may include transferring the data in readable formats, such as an Excel spreadsheet or word document.

Tools for scraping can be quite different. The significant difference is in the design restriction of the targeted web source. When approaching the creation of the tool, various design factors are to be kept in mind. Moreover, if a non-programmer uses the tool, it should be straightforward yet intuitive, and most importantly, it should return data in a usable form.