I’ve been exploring what online discussions look like when you break them down into individual claims instead of treating posts as a single unit.

The screenshot shows how a specific sentence can be highlighted and evaluated directly.  People can agree, disagree, or react to that exact part of the text.

What’s interesting is that this doesn’t just change the interaction model, because it also changes the data that gets generated.

Instead of only having signals at the post or comment level, you now have voting data tied to specific phrases and statements across all discussions.

That makes it possible to start asking questions like:

– which types of claims tend to receive the most agreement or disagreement?

– where do similar phrases consistently lead to contention?

– how do certain ideas evolve as they get refined or reworded over time?

Over time, this produces a structure where:

– some claims converge (broad agreement)

– some remain contested

– some get refined or replaced

The idea is to make it easier to both understand discussions in context, and analyze patterns in how people respond to ideas at a much more granular, and global level.

Curious whether this kind of claim-level data would make complex discussions easier to interpret — or even measurable in a way they currently aren’t.

[OC] What might voting data looks like when people can vote on individual sentences instead of entire posts?