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Show HN: Slop or not – can you tell AI writing from human in everyday contexts?

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Show HN: Slop or not – can you tell AI writing from human in everyday contexts?

#1

Post by eigen-vector »

I’ve been building a crowd-sourced AI detection benchmark. Two responses to the same prompt — one from a real human (pre-2022, provably pre prevalence of AI slop on the internet), one generated by AI. You pick the slop. Three wrong and you’re out.

The dataset: 16K human posts from Reddit, Hacker News, and Yelp, each paired with AI generations from 6 models across two providers (Anthropic and OpenAI) at three capability tiers. Same prompt, length-matched, no adversarial coaching — just the model’s natural voice with platform context. Every vote is logged with model, tier, source, response time, and position.

Early findings from testing: Reddit posts are easy to spot (humans are too casual for AI to mimic), HN is significantly harder.

I'll be releasing the full dataset on HuggingFace and I'll publish a paper if I can get enough data via this crowdsourced study.

If you play the HN-only mode, you’re helping calibrate how detectable AI is on here specifically.

Would love feedback on the pairs — are any trivially obvious? Are some genuinely hard?

Show HN: Slop or not – can you tell AI writing from human in everyday contexts?
slop-or-not.space

Re: Show HN: Slop or not – can you tell AI writing from human in everyday contexts?

#2

Post by lucastonelli »

Hey, congratulations on the final product. It even feels fun. Some are really hard, but some feel blatantly obvious. I don't know why though. I guess it's just because the way we communicate feels off when compared to AI, some times.

Re: Show HN: Slop or not – can you tell AI writing from human in everyday contexts?

#3

Post by SsgMshdPotatoes »

Nice idea! Em dashes were giveaways for AIs and typos for human, at least in the ones I did, so those are at least trivial. So might have to do some filtering at least for those.

Some were hard though, yeah (at least if not looking longer than 5-10 seconds). Btw, it seemed more logical to me to just see a green/red card when you click, i.e. right choice or wrong choice. Getting red for the correct answer confused me a bit (but this might just be me).

Re: Show HN: Slop or not – can you tell AI writing from human in everyday contexts?

#4

Post by eigen-vector »

lucastonelli wrote: post #2

Hey, congratulations on the final product. It even feels fun. Some are really hard, but some feel blatantly obvious. I don't know why though. I guess it's just because the way we communicate feels off when compared to AI, some times.

Thanks for checking it out! The obvious ones are (hopefully) weaker models :) but yes my experience has been unless you're engaging with human written content consistently the line really blurs easily.

Re: Show HN: Slop or not – can you tell AI writing from human in everyday contexts?

#5

Post by lucastonelli »

SsgMshdPotatoes wrote: post #3

Nice idea! Em dashes were giveaways for AIs and typos for human, at least in the ones I did, so those are at least trivial. So might have to do some filtering at least for those. Some were hard though, yeah (at least if not looking longer than 5-10 seconds). Btw, it seemed more logical to me to just see a green/red card when you click, i.e. right choice or wrong choice. Getting red for the correct answer confused me…

The coloring is a fair point. I was some times confused if I got the right or the wrong one XD

Re: Show HN: Slop or not – can you tell AI writing from human in everyday contexts?

#6

Post by eigen-vector »

SsgMshdPotatoes wrote: post #3

Nice idea! Em dashes were giveaways for AIs and typos for human, at least in the ones I did, so those are at least trivial. So might have to do some filtering at least for those. Some were hard though, yeah (at least if not looking longer than 5-10 seconds). Btw, it seemed more logical to me to just see a green/red card when you click, i.e. right choice or wrong choice. Getting red for the correct answer confused me…

Thanks for checking it out! The color signal is useful feedback. Let me think about it and rework!

Yeah there are some very obvious tells, but the models that are most capable are very good at writing like human.

Especially when the human responses for reddit or HN prompts were presumably made after reading the content of the article or the post; whilw the model is simply going off of the title.

Re: Show HN: Slop or not – can you tell AI writing from human in everyday contexts?

#7

Post by SsgMshdPotatoes »

SsgMshdPotatoes wrote: post #3

Nice idea! Em dashes were giveaways for AIs and typos for human, at least in the ones I did, so those are at least trivial. So might have to do some filtering at least for those. Some were hard though, yeah (at least if not looking longer than 5-10 seconds). Btw, it seemed more logical to me to just see a green/red card when you click, i.e. right choice or wrong choice. Getting red for the correct answer confused me…

Also for example this one has a giveaway for the human case: "There are lots of great people here at /r/personalfinance" (actually, not sure if that is a giveaway, that was my guess, but depends on how the model was prompted, I guess). And human ones often seem to have two spaces sometimes instead of one, idk why. If you want to get a serious dataset, maybe you could use this one to find all the flaws and perfect it, and then try to get a real dataset from the next one? People will be more eager to help too if they've seen you designed it all very carefully. (Or you could filter the results from this one to make it a good dataset if you get lots of responses.)

Re: Show HN: Slop or not – can you tell AI writing from human in everyday contexts?

#8

Post by eigen-vector »

SsgMshdPotatoes wrote: post #7
SsgMshdPotatoes wrote: post #3

Nice idea! Em dashes were giveaways for AIs and typos for human, at least in the ones I did, so those are at least trivial. So might have to do some filtering at least for those. Some were hard though, yeah (at least if not looking longer than 5-10 seconds). Btw, it seemed more logical to me to just see a green/red card when you click, i.e. right choice or wrong choice. Getting red for the correct answer confused me…

Also for example this one has a giveaway for the human case: "There are lots of great people here at /r/personalfinance" (actually, not sure if that is a giveaway, that was my guess, but depends on how the model was prompted, I guess). And human ones often seem to have two spaces sometimes instead of one, idk why. If you want to get a serious dataset, maybe you could use this one to find all the flaws and perfect it,…

You'd be surprised at the nuances we tend to miss :)

This time around I prompted the models not necessarily to be adversarial - i didn't ask them to try and fool the reader. But i gave them contextual info - something to the effect of "you're a user posting on hacker news"

Re: Show HN: Slop or not – can you tell AI writing from human in everyday contexts?

#9

Post by SsgMshdPotatoes »

eigen-vector wrote: post #8
SsgMshdPotatoes wrote: post #7

Earlier quoted context omitted.

Also for example this one has a giveaway for the human case: "There are lots of great people here at /r/personalfinance" (actually, not sure if that is a giveaway, that was my guess, but depends on how the model was prompted, I guess). And human ones often seem to have two spaces sometimes instead of one, idk why. If you want to get a serious dataset, maybe you could use this one to find all the flaws and perfect it,…

You'd be surprised at the nuances we tend to miss :) This time around I prompted the models not necessarily to be adversarial - i didn't ask them to try and fool the reader. But i gave them contextual info - something to the effect of "you're a user posting on hacker news"

True, if you look for all "obvious patterns" and filter those out of the dataset, not much will be left probably. Maybe the best is then to just publish as complete a dataset as possible, so all questions, all user answers, for each user the nr of questions they did, time for each question, etc. Then people using that dataset can draw their own conclusions.
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