The Filter Bubble Problem

We recently shared this New York Times article since it echoed the concept we’ve been discussing in the Eyesover office over the past few months. As the article points out, the concept of the Filter Bubble was never more evident than during the US Presidential election, and the outcome seems to have taken the issue to even greater levels.

What is the Filter Bubble? It is the concept of an individual’s self-selected media feeds reinforcing one’s own beliefs. Who we choose to follow on Twitter, like on Facebook, or generally subscribe to online will more times than not, consist of sources that provide content we agree with. If one did not support Trump, one tended to populate their feeds with content provided by others that held the same view and a similar scenario played out for those who did support the now-President.

The problem is that there is rarely any crossover in these feeds and a bubble is formed where the views of an opposing side of a debate are not only silent, as the bubble is continually reinforced, we soon become unaware those views even exist.

When we look at the tools we use to monitor online media, the same selection bias flaw is inherent in most systems since the foundation of the monitoring process are the keywords and search terms input by the user. No matter how balanced an individual tries to be, search terms that come from users are going to be affected by personal bias towards the topics being monitored.

That is why at Eyesover we’ve been working on developing and continually improving our Issue Discovery features. We firmly belief online media monitoring will provide users with a much more robust and complete view of issues and content when it is the data, not the user, that drives the monitoring process. It is obvious that if we are monitoring a sector or an issue, we want all views brought into the mix, not just those we find acceptable or even tolerable.

Our system uses the content found online to provide terms and keywords required for monitoring in order to remove any potential for selection bias that could cause the system to miss critical information or trends.

Why was Trump’s win such a surprise to so many? We suspect it was due to people not being aware that Trump and Clinton had similar levels of support across the country. As online sources rapidly become many people’s go-to source of information, the feeds these sources provide users reinforce the feed owner’s belief that most were supporting “their” candidate and they saw no evidence of support for the “other” candidate.

The Eyesover system is designed to overcome this very real problem of selection bias and like it or not, show users the full range of opinions and discussions on the issues of the day in any sector so they are better informed to make critical decisions.

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