Many YouTube users find reaction videos to be low-effort or irrelevant, yet these videos often dominate search results and recommendations. This clutter makes it harder to discover original content, degrading the experience for those who prefer other types of videos. Since reaction content is a growing category and YouTube's algorithm favors high-engagement videos, the problem is unlikely to resolve itself.

How a Filtering Tool Could Work

One way to address this could be a browser extension or third-party app that filters reaction videos from search results and recommendations. The tool might analyze titles, descriptions, and metadata to identify reaction content, then remove or collapse those videos from view. Users could adjust settings to control how aggressively the filter works—for example, blocking only explicit "reaction" videos or also including related formats like commentary. Over time, machine learning could improve accuracy by learning from user feedback, and the tool could expand to other platforms where reaction content is prevalent.

• Basic version: Keyword-based filtering (e.g., hiding videos with "reacts to" in the title).
• Advanced version: AI classification to catch subtler reaction content, plus customization for blocking specific channels or video styles.

Why This Could Succeed

Unlike general-purpose blockers, this tool would focus specifically on reaction videos, automating a task that users currently handle manually (if at all). It could also benefit creators of original content by reducing competition in search results. While YouTube might resist third-party modifications, positioning the tool as a personalization feature—not a threat to revenue—could mitigate pushback. Early adopters might include educators, professionals, or anyone frustrated by the overabundance of reaction content.

For testing, a simple prototype could gauge whether users find the tool valuable and whether reaction videos can be reliably identified. If successful, the idea could evolve into a broader platform for content customization, giving users more control over their viewing experience.