The Algorithmic News Feed Problem

And The Filter Bubble

Facebook has been at the center of fake news stories and the resulting negative PR because its entire business model depends on and supports ad tech.

Ad money relies on views and clicks. So the more Facebook users “Like”, react and engage with content in the news feed, the more data gets fed into Facebook’s algorithms which are designed to put “engaging” posts and content in front of users. In other words, a filter bubble is naturally created to feed users with content that they believe in or support. Biases, prejudices and fake news get thrown into the mix if the algos think you’ll engage with it.

Filter bubbles are a real concern because they describe a condition that feeds us with news or content that we likely already agree with and which will reinforce our own belief system (right or wrong). So, the danger is not only are we are not exposed to other opinions, but even worse, false news stories proliferate and go unchecked (often appearing to be legitimate with lots of engagement numbers from other like minded users, paid or not).

This is the reason some avoid algorithmically ordered feeds. That plus the real world problem that such feeds often result in missing friend posts that we would normally want to view—despite claims to the contrary.

Really, this is just a reminder to get your news from a variety of sources. Do not rely on Facebook or other ad tech platforms to provide you with important, or even factually trivial, news bits that will benefit your opinions.

Posts that get re-shared, liked, loved, retweeted, hearted or replied to are not always accurate or organically engaged with. There can be lot of paid for engagement that influence the algorithms.

One of the areas that Twitter excelled at, even though algorithmically ordered timelines have appeared on official apps, is the reverse chronological display of tweets regardless of engagement metrics. This can still be experienced in third party apps like Tweetbot where a raw reverse chronological feed, unfiltered by hidden algorithms, persists.

In many respects the quest for ad tech dollars has altered the type of content that is labeled “news” and contributes to filter bubbled perceptions of the real world. Social media platforms (generally supported with ad tech) allow this situation to persist while occasionally applying Band Aid solutions for appearances.

It’s just another area that the ad tech tentacles have squeezed—always trying to monetize whatever data it can touch, even if it means fostering innacuracies.

I think at some point, the filter bubble effect of algorithmic feeds will cause users to revolt in much the same way that they are doing now with ads which serve as the catalyst for a growing usage of ad blockers. At the end of the day, ad tech is responsible for the ad block problem for publishers and is at the root of the bubble effect too.

Can the problem be fixed? Sure. However, it needs to start at the root cause which is ad tech—a beast that no one has tamed yet.

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