Who Gets Caught in Online Echo Chambers?

If you’re on social media, you’re probably experiencing an online echo chamber. You’re seeing content and opinions primarily posted by friends and by the media outlets you follow. Like a real-world echo chamber, these messages bounce ideas and information to you that are similar to the ones you put out into the world.

While it’s commonplace to connect with like-minded people in social settings, echo chambers are exacerbated as more social interactions shift online. You may realize that your feeds are biased, but the impact of personalization algorithms — used, for instance, by Facebook — can be subtle and multiplicative. These algorithms lead to even more personalization over time. The chambers become deeper as the algorithms and the choices users make about what to read and watch mutually reinforce each other. The trend makes it harder for dissenting views or entirely new information to permeate our online worlds, and it reinforces our own beliefs and information sources.

Many businesses conduct marketing activities and develop their brands in online environments that highly manage the content that users encounter. Executives may not be aware of the extent that algorithms are influencing this experience. Understanding the context in which messages are being delivered is a critical first step. More so, our research shows that there are ways that the echo chamber itself might be managed.

The Interests of the Crowd vs. the Interests of the Individual

For our research, we examined two things: the types of users most likely to get caught in content echo chambers, and the role of the content’s popularity — such as “like” counts and view counts. We created a simple online environment for content exploration that broke down search into two dimensions — content topic and content popularity. We then looked at the ways that individuals moved through the material. We observed, for instance, the weight that users placed on their own interests versus those of the crowd, and how these patterns relate to an individual’s characteristics, such as how social they regard themselves and whether they try to influence others in their social circles (what we term “opinion leaders”).

Our assumption was that users who conduct little exploration and rely more heavily on the crowd will, over time, see less diverse content.