The problem with algorithmic news feeds

It might seem overwhelming to navigate the many sources of news in our lives, from social media to traditional newspapers such as the New York Times.

But ultimately I’ve decided there are only two types of news feeds: Those that are uniformly served to everyone after being chosen by a committee of editors; and the second type that are served up algorithmically and customized to each reader’s digital footprint. By “digital footprint,” I’m referring to the unique profile of our browsing habits, location, and all of the other data collected by websites, advertisers, technology companies, social media, and other apps.

The first type, which I’ll call “traditional,” ignores data. I’m not certain this exists electronically anymore, so the safest way to ensure you are reading a traditional news source is to pick up a piece of paper, like a newspaper or magazine. Or perhaps another way would be to mask your ip address, and browse anonymously.

Traditional news sources are not bias tree. They too are targeted, but instead of targeting an individual they take a wider approach toward particular reader groups. But they do not dynamically swap content in and out, varying its importance to the point that news value is adjusted based on reader preference.

The second type of news is “algorithmic.” An example of algorithmic news is when I listen to a song by an artist on YouTube music, and later while scrolling through a list of top national news stories, I discover a small article about the same artist inserted between two topical stories of the day.

What is my problem with this?

My problem is that the small article about the artist whose music I just listened to has been weighted to appear as a story of prominence to many people, when in fact it is only a story of prominence to one person – myself.

As this continues over weeks and months, I am fed a diet of news that targets me and manipulates me into thinking each article is important to everyone else. It continues a cycle of reinforcing my own interests. I think of this as “self radicalizing.” If I love the music of the Beatles, I am going to hear more about the Beatles as I scroll an algorithmic news source, thereby expanding my knowledge and interest about the Beatles.

The algorithm killed my fresh perspective

In that moment of searching the news, I was not looking for The Beatles. I was looking for topics of general appeal, such as breaking national news, or celebrity gossip. I simply wanted to know what was going on in the world. Instead, the algorithm fed me articles based on my recent searches or my digital profile, tainting my quest for fresh information and fresh perspectives.

And this traditional versus algorithmic dynamic affects more than my news. Algorithms form the basis of services such as Spotify, which feeds me a restricted diet of music I am sure to love. Whereas the traditional sources of music (radio and record stores) offer a uniform delivery of fresh musical ideas to each patron who listens in, or wanders into the store.

I think there are many who will disagree with my preference for traditional news and music sources. But I prefer not to lose control of my ability to change my mind. I’d rather not have an algorithm laboring to keep my mind contained to things I already like.