Ibrahim Ahmed

Principal Component Analysis

Escaping Echochambers

Oct 20, 2017 | 11 min read
Categories: Machine Learning,
Tags: Machine Learning, Principal Component Analysis, Visualization,

The echochamber effect is a worrisome issue in social media. It risks isolating users in exclusive groups as they repeatedly subscribe to content that reinforces their biases. To keep users engaged, websites expose users to content similar to their history. You will get recommendations for movies you may like, or peoply you may befriend, or communities you may join - all based on some measure of similarity with your profile.

On first glance, this seems convenient: a user does not need to explicitly search for content. The digital platform assumes a user’s wants. You may even be shown interesting things you wouldn’t have known to look for. As users consume more content, their digital footprints become more accurate descriptors of their preferences. It becomes easier to navigate the world wide web: users’ interests are served on a platter. However, this is not a unitateral benefit. An exchange is being made in the currency of data: convenience for autonomy. At some point, users may find themselves in concord with everything they see online. Gradually, all their new Facebook friends share their views. According to news articles on their social feeds, the world seems to be going in the exact direction they predicted. Every new movie recommendation is a romcom - their guilty pleasure. Uncanny.