Welcome to the fourth issue of "Weekly Insights." This week's topic is "Are you being trapped in a filter bubble?"
Topic: Are You Being Trapped in a Filter Bubble?
What is a filter bubble (information cocoon)?
If you keep browsing content about a single topic on platforms like TikTok or Bilibili, your homepage recommendations will soon be flooded with videos about that topic. I'm sure you've experienced this. The logic behind it is the recommendation algorithm at work, similar to Taobao's "Guess What You Like" feature.
My attitude toward this is neutral. On one hand, these recommendations sometimes save me time finding similar videos, and they also give content creators traffic boosts, increasing their exposure. On the other hand, I sometimes resent it because these recommendations accelerate my aesthetic fatigue. At the same time, they reduce my chances of discovering interesting new information – the recommendation algorithm encloses me within a certain domain. I feel like a silkworm, wrapped up in a cocoon made by technology.
Letting recommendation algorithms create a cocoon around you is dangerous. It narrows your vision, making you immersed in areas you're interested in while knowing little about other fields. Although in the internet age, with search engines at our disposal, we don't need to become polymaths – we can solve most problems by asking search engines. But learning about other fields is never a bad thing.
How can we avoid being trapped in a filter bubble?
- Actively train the recommendation algorithm
We can use the characteristics of recommendation algorithms to our advantage by browsing videos from different fields that interest us. This way, when the algorithm makes recommendations, it has more options to choose from. Actively training the recommendation algorithm requires you to change your self-positioning. Although the algorithm recommends information to you passively, this recommendation is based on your active training, making your acceptance pseudo-passive.
- Actively seek information
In your spare time, follow blogs, official accounts, and newsletters from internet experts. Their writings may occasionally recommend interesting new things, and you can follow their guidance to continue searching until you feel there's no need to continue – similar to depth-first traversal in data structures.
For example, sometimes based on a blogger's hyperlink, I'll go to a GitHub repository and briefly look at the repository description. If I think the project is great, I'll give it a star. Then I'll check out the repository owner's GitHub profile, and if there's a link to their personal website, I'll click through to explore... In short, stop searching when you feel it's time to end.
The following is a quote I read in a newsletter that resonated with me. In summary, whether actively training the recommendation algorithm or actively seeking information, the key word is "active" – avoiding filter bubbles effectively requires you to be proactive rather than completely passively accepting algorithm recommendations.
Quotes
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Interest is the best teacher. If you don't have interest, cultivate it; otherwise, don't enter this industry.
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Small habits make it easier for us to persist and build confidence in ourselves.
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Work hard to change what can be changed; peacefully accept what you're powerless to change.
Link Sharing
What is NSFW?
The above site can identify inappropriate images. I heard from friends in a Telegram group that this site cannot accurately identify pornographic images related to anime characters.
The image below shows my test of the site using a photo of a foreign beauty. The pornographic rating was 0.08%. Feel free to test your treasured beauty photos for their pornographic rating. 😄😄😄
A fun browser game where a cow hides on the screen. The closer your mouse gets to the cow, the louder its sound becomes. Hint: The mouse cursor will eventually change shape. 👆
Click again to find the hidden cow. 🐮