Chapter 18

Using Data Science in Journalism

In This Chapter

arrow Defining the who, what, when, where, why, and how of a data-driven story

arrow Figuring out tricks for collecting data for your story

arrow Finding the stories after you have your data

arrow Presenting your data-driven story in a way that makes sense

You’re not interested in yesterday’s news, right? Well, neither is anyone else. That’s why traditional media is in a sink-or-swim situation. The journalism field and traditional media industry have undergone countless changes since the explosion of the Internet. Although the industry was once dominated by print and television, the growth of digital media technologies continues to introduce irreversible changes.

As the adoption of digital media like Twitter, Facebook, and blogs continues to grow, media’s response-time requirements get shorter and shorter. If you’ve already heard about the latest news through social media — or through word-of-mouth from someone who heard it on social media — then why would you spend time reading it in a paper or watching it on TV? It’s most likely that, if you’re ...

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