Data Science on Why Reading the News Might Stress You Out
By: A Samuel Pottinger
Tags: Machine Learning, Sentiment Analysis, NLP, News, Data Visualization

Visualization of individual articles published by each news source over roughly one day (Aug 20th–21st 2019 because timezones). Code for generation below.

While preparing a paper recently about linguistic patterns in the news media, I found myself reading through many of the articles my neural network processed, trying to understand its findings through the lens of specific stories. Having seen writing from many different sources and having digested views from across the political spectrum, I can confirm first-hand that consuming the news is exhausting with every moment feeling like it expresses a constant sense of urgency.

Starting with the data, “sentiment” roughly refers to how strongly positive or negative the words are within a piece of text. In the context of this essay, the texts are article “descriptions” seen in news agencies’ RSS feeds (often snippets from the articles themselves or the short preview / summary text seen on the publisher’s website). Assigned through TextBlob’s natural language processing sentiment model, a value of -1 means that it is as negative as it can get, 1 means that it is as positive as it can get, and 0 is neutral. It is worth noting that sentiment can be contextual (and nuanced) so magnitude of polarity may be more important than direction in this case and, since the sentiment model is not news media specific, the calculation isn’t perfect. Still, as shown in the visualization above, some agencies see different distributions in article sentiment with some like Vox or the Wall Street Journal being quite narrow (more neutral sentiment) while others like the Daily Mail or CNN explore more “polar” parts of the linguistic spectrum. This is not a criticism of any agency or commentary on the necessity of more polar word choice but it highlights that, given the news of the day, not only are different agency’s coverage of the world more “intense” than others (in terms of word choice) but, on a random day, one can experience many extremes of sentiment polarity across the media landscape. Indeed, a quick analysis of a random month (June) shows that, in each day, the range tends to be wide with many days seeing at least one article with an average sentence polarity of 1 or -1.

Most days of June 2019 saw a wide sentiment polarity range across the 10 news agencies examined (listed in the visualization above).

Perhaps we do live in a time of great acceleration with pivotal moments always happening but, on the other hand, constant extreme polarity could be troublesome because, as Jon Stewart observes, “[i]f everything is amplified we hear nothing.” That being said, this analysis puts into context the anxiety caused by polarity in the news: data confirm that at least part of the media landscape does marinate in a prolonged state of heightened sentiment and, because it sees acute language, it may be understandable how the news causes apprehension. I’m not sure what to do about that yet though it is worth critically examining the “polarity” of the language to which you are exposed through your media consumption habits. However, like with my experience of seeing the trends that outline this landscape, I hope that your seeing these data provide a sense of perspective and, maybe, even calm.

Code and data at https://github.com/sampottinger/news_flower. Article edited to help readers better undertand limitations of using an pre-existing sentiment model.