Twitter as a Data Source

Twitter is one of the easiest social media platforms to mine for data, and it is an excellent resource used every day by social scientists for research. We’re going to use Twitter as the central example in this course, but the techniques that we will show you are applicable across any social media platform. In this video tutorial, you’ll see how easy, interesting and productive this process can be for you to implement.

What does Twitter data represent?

The first and most well-known characteristic is its brevity and simplicity. The footprints left by its users while interacting with each other can be collected and analysed by downloading data into a spreadsheet, then tweets can be turned into rows and columns, ready for analysis. In other words, status updates, which are mostly words and pictures, can be turned into “statements” with specific characteristics: authorship, content, performance, time, and so on. As an example of its richness for data mining, the metadata in each tweet contains not only the text but also forty-five different variables, such as number of followers, favourites, language, geographic location, etc.

What are the main limitations of gathering analytics data from Twitter?

• Data retrieval bias: Data gathering from online social networks like Twitter requires a great deal of computational knowledge and computer programming.

• Representation bias: Twitter usage differs significantly across the world. These differences in usage within different countries make it challenging to take Twitter as a representative sample of the general population.

• Language bias: Researchers have found that Twitter hashtags are focused on only a few languages. This might misrepresent data as the full data of an event cannot be captured in all languages.

Read the article attached below for more insights into the benefits and limitations for analysing Twitter data.