Abstract Title
Hidden Associations: Observing Connections in Data Published Online Using a Web Scraper and Graphing Software
Abstract
Access to information has become a given since the advent of the internet. When this information is text-based (e.g. online books, magazines, articles, etc.) we consume it by reading. Though this process is able to tell us what the author thinks about the subject, it’s very inefficient at providing us with other information, such as how the author writes, what words the author favors, and so on. For instance, if we were to search Twitter for “NBA”, scrolling through the results would be a very inefficient way of getting an idea of what the public is talking about in relation to the NBA. Thankfully, this is not necessary. We present software that incorporates a web scraper to gather online data and a program that processes and graphically visualizes this data. The resulting visualization is not difficult to interpret and can be used to gauge public opinion.
Hidden Associations: Observing Connections in Data Published Online Using a Web Scraper and Graphing Software
Access to information has become a given since the advent of the internet. When this information is text-based (e.g. online books, magazines, articles, etc.) we consume it by reading. Though this process is able to tell us what the author thinks about the subject, it’s very inefficient at providing us with other information, such as how the author writes, what words the author favors, and so on. For instance, if we were to search Twitter for “NBA”, scrolling through the results would be a very inefficient way of getting an idea of what the public is talking about in relation to the NBA. Thankfully, this is not necessary. We present software that incorporates a web scraper to gather online data and a program that processes and graphically visualizes this data. The resulting visualization is not difficult to interpret and can be used to gauge public opinion.