Modern online discussions are often plagued by uninformed comments, as many participants react to headlines or snippets without reading the full article. This habit undermines meaningful discourse, spreads misinformation, and frustrates both readers and content creators. A potential solution could involve implementing a system that tracks whether commenters have actually engaged with the content before they post.

How It Could Work

One way to address this issue is by assigning a "reading status" badge to comments, indicating whether the commenter likely read the article. This could function through:

• Browser extension or plugin: Tracks user behavior—such as time spent, scrolling depth, or interaction with key sections—and then displays a badge (e.g., "Verified Reader") alongside their comment.
• Website integration: Publishers could embed lightweight scripts to measure engagement and automatically flag comments based on reading behavior.

The badge could be binary ("Read/Unverified") or tiered ("Skimmed," "In-Depth Read"), adjusted dynamically depending on article length and complexity.

Why This Matters

A system like this could benefit multiple stakeholders:

• Content creators would see fewer low-effort comments and more substantive discussions.
• Readers could filter out drive-by reactions, focusing only on informed contributions.
• Platforms and moderators would spend less time managing toxic or irrelevant comments.

Some users might initially resist tracking, but a transparent, opt-in model—combined with incentives for credibility—could encourage adoption.

Execution and Growth

A minimal viable product (MVP) could involve:

1. Developing a browser extension that tracks reading behavior and badges comments on major news sites.
2. Partnering with niche platforms (e.g., Substack, Medium) to test effectiveness.
3. Offering an API or SDK for wider publisher adoption.

Privacy would be a key concern, so tracking would need to be consensual, anonymized, and easy to pause. Gaming the system (e.g., fake scrolling) could be countered by detecting unnatural reading patterns and adjusting verification thresholds dynamically.