I’ve been quietly building an experiment called Society Speaks to explore a question that’s bothered me for a long time:

Can we measure public opinion without flattening it into polls, comments, or outrage?

Instead of comments or demographics, the system uses: • clear statements • simple voting (agree / disagree / unsure) • optional short explanations

People aren’t grouped by who they say they are, but by how they actually respond. Using ML techniques inspired by Pol.is, this reveals natural opinion clusters, areas of genuine consensus, ideas that bridge different groups, and honest fault lines where agreement doesn’t yet exist.

There’s a small daily entry point (inspired by Wordle): one question per day, ~2 minutes to answer, insight revealed only after you respond. Over time, this creates a longitudinal, more nuanced view of public opinion than polls or comment sections usually provide.

We also take curated news articles and podcasts and turn them into structured prompts for deliberation, rather than reactive debate.

This isn’t a finished product and I’m not claiming it “solves” democracy. I’m mostly interested in whether this approach is: • methodologically sound • useful compared to traditional polling or forums • flawed in ways I haven’t spotted

If you’re curious, today’s question is here: https://societyspeaks.io/daily

I’d genuinely value critique, especially from people who’ve worked on polling, deliberative democracy, or large-scale opinion systems.