How Smart is Your News Source?

Readability Analysis of 21 Different News Outlets

I think it’s more important than ever to understand the perspectives and biases of our new sources. Unfortunately there is just so much news¹ that it is almost impossible for us to escape our tiny filter bubbles.

Luckily, the same technology that got us into this mess, can help us navigate it. Using computers, it’s possible to get a broad view of multiple news sources and to see what areas they focus on most. It’s also fun to see how the writing styles of different outlets differ. While we’ll need many more advancements in natural language processing (NLP) to really get a handle on news bias, there are some fun analyses we can do now.

Towards that end, I’ve used the python Newspaper library² to collect as many articles as I could from 21 differnet news outlets over the past 6 months. Here are some interesting ways that they differ.

Sentiment of the News

One of the easy and interesting things to look at when it comes to news is story sentiment. Using the python VADER library³, we can score all stories from different publications and measure what their average sentiment is. Positive numbers indicate more upbeat language, while negative scores suggest dark and negative writing.