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Wikipedia, the most visited site in the United States and one of the top five globally, is home to tons of useful data. From in depth lists of topics to celebrity pages, there is lots of information to use in a wide variety of industries. Therefore learning to collect data from Wikipedia is a skill that everyone should learn regardless of profession. Web scraping Wikipedia, the automatic extraction of data from the Wikipedia site, is the best way to collect data for analysis or other purposes. Scraping Wikipedia data is great for people managing the reputations of brands or celebrities, journalists keeping track of elections, and anyone looking for information on which Wikipedia pages are the most popular or have the most links. Additionally, our Scraping Robot team can help you create a custom scraping solution to get the exact kind of data you need.
What is Wiki Data?
Wiki data is any data that can be extracted from the Wikipedia website such as
- Current events (recent elections, trials, notable deaths, sporting events, etc)
- Page topics
- Information within topic pages
- Featured articles
- Most important articles
- Third party reference materials
- List pages
With the variety of data, collecting Wiki data is beneficial to people of all backgrounds across industries. While many of our teachers joked that Wikipedia is not a good academic resource, Wikipedia pages’ sources and references are an easy way to find academic journals and papers you might’ve never found otherwise. Whether you’re extracting data from Wikipedia itself or using it to discover other data sources, learning to scrape Wikipedia and identify useful data within the website is an important skill no matter your field.
Collecting Wiki Data
Like other online data, you can manually extract data from Wikipedia. However, the manual data extraction process takes lots of time, money, and employees. Web scraping, the automatic extraction of data from a web page, is a quick and easy process. Once collected, the data can be organized and analyzed. Using a Wikipedia scraper makes it easy to access tons of useful information without having an entire data department within your organization, practice, or even your home. Therefore scraping is ideal for small businesses or teams that don’t have lots of resources to dedicate to data analysis while also ideal for larger organizations that want to save their workers’ time spent on data extraction and increase analysis.
How to scrape Wikipedia
When learning how to scrape Wiki data or how to scrape columns from wikipedia, it is important to understand the difference between a generic scraping tool and one built for Wikipedia. A HTML scraper turns any webpage you input into data as output. This makes it easier to organize the data for analysis. Scrapers specifically built for Wikipedia or other websites are able to recognize and organize the data.
Benefits of Using a Wikipedia Scraper
There are so many different kinds of data you can extract from Wikipedia. Here are just a few examples of benefits from web scraping wiki data.
One of the reasons our teachers forbid us from using Wikipedia is because pages can be edited without much oversight, therefore resulting in false additions to the pages of celebrities or historical figures. Because of this aspect of Wikipedia, it is important for those who represent celebrities, brands, organizations, and politicians to detect and correct false additions right away. Scraping makes it easy to spot the day to day changes in a given page. It is especially important to keep false information off Wikipedia because the site is one of the most visited. If someone is looking for basic information on a celebrity or a brand, Wikipedia is often one of their first stops.
In addition to simply removing the falsehoods from a Wiki page, it is equally interesting to analyze the kinds of falsehoods people are regularly spreading about a certain figure. In a backwards way, analyzing this information is a sort of sentiment analysis. For example, people have added celeb names to the “God Complex” page under “see also.” While a joke, it can also provide insights into public opinion of a certain celebrity or topic.
Keep track of world events
On the Wikipedia homepage, there are lists of recent elections, trials, deaths, sporting events, and much more. For journalists trying to discover story ideas to stay updated, scraping this page regularly provides an outlook on many different aspects of current society. If you’re looking for story inspiration, seeing all the recent events laid out as data makes it easier for interesting ones to catch your eye. You can even organize the data by most interesting to cover for your particular beat in order to keep track of multiple ideas. If you’re a breaking news reporter, you can scrape current events data to ensure your own sources were complete and to fill in any gaps that may exist.
Current events data is useful for more than just journalists. If you’re trying to predict instability in certain regions that can cause disruption in your industry, then keeping track of relevant events avoids you being caught off guard. Being more prepared helps you make data based decisions in the future instead of always reacting to things as they come.
The special pages tab on Wikipedia brings you to multiple categories such as dormant pages, dead end pages, uncategorized pages, and more.
You can use the category tree to get a list of pages by category and scrape that list. For example, if you search “Education in the United States” in the category tree, the resulting tree has drop down topics of pages related to larger categories of education by county, student protests in the United States, Film about education in the United States, and other relevant topics. If you’re a student or researcher trying to discover related issues to your field or research or even brainstorm different paths of research.
Another great feature to scrape are the High Use Pages lists. These lists include information about the most revised pages, most linked pages, pages with the most categories, most revisions, and other data points that can be used in research.
Beyond Wikipedia Web Scraping
Web scraping Wikipedia is a great place to find data, but there are other sites well built for scraping such as social media sites. Building a custom scraping project with Scraping Robot allows you to create a data extraction process built for your data needs.
With regard to reputation management, Wikipedia edits are only one part of the story. Social media sites such as Twitter can be scraped for mentions, trending topics, hashtags, and more. When you scrape your Twitter mentions, you’ll see each time your account has been mentioned in a tweet. This provides direct feedback similar to online reviews. For organizations with a known brand, these mentions or trending topics generally can also be related to an organization’s labor practices, public reputation, product reputation, scandals, etc. Therefore, if you are looking to do management reputation or any kind of public relations, it is extra important to scrape Twitter to get ahead of any growing stories, trends, or online discussions.
Custom scraping project
Building a custom scraping project with Scraping Robot is best for organizations with unique data needs. After an initial discussion of your data needs (type, frequency, amount), we will create a project proposal. Once all the terms are agreed upon, you’ll have the expertise of the Scraping Robot team to build a scraper that can handle larger sizes of data, unique kinds of data while still being quick and cheap in comparison to manual data extraction. If this sounds ideal for your next project, check out our process page for more information.
Being one of the most visited sites in the world positions Wikipedia in a unique space in online data extraction. With seemingly endless pages, categories, sub-categories, and references, there is bound to be data relevant to any industry or project that may arise. Using a web scraping tool that automatically extracts data from Wikipedia is the best way to save time, money, and labor. Web scraping Wikipedia makes it easier to keep up with current events, discover new topics or categories of research in your field, and manage the reputation of brands or celebrities. While our HTML scraper works well with Wikipedia, Scraping Robot’s customer scraping projects are a collaborative effort between our team and yours that ensures all your data needs are met.