u/mintlite4

Student project, not selling anything. I'm building a model that decides whether a review is genuine or planted, and I'm trying to put real numbers on the cost of each kind of mistake.

The one I can't find data for is the false positive a real customer leaves an honest review and an automated system hides or removes it.

If that's happened to you: did you notice, and how? Did it measurably hit conversion or ranking, or was it just irritating? How long until things recovered? Was there any route to get it reinstated?

Someone told me the impact depends heavily on total review volume — a store with 40 reviews feels a removal far more than one with 4,000. I'd like to know if that matches your experience, because right now my cost model rests on a single conversation.

Following up on a conversation here last week. Someone made the point that the cost of a wrongly removed review depends on how many reviews the business has in total, which changed how I weight errors in a small model I'm building for a class.

What I still can't resolve: a genuinely furious customer and a paid competitor review can read almost identically. Short, one star, no specifics, emotional.

For anyone who reads a lot of reviews — is there anything in the text alone that reliably separates the two? Or does it only become obvious once you can see the account, the timing, or a cluster of similar reviews?

My model deliberately sees only the text and the rating, and I'm starting to suspect that constraint makes the task impossible rather than just hard. I'd rather find that out now.

Student project, not selling anything. I'm building a model that decides whether a review is genuine or planted, and I'm trying to put real numbers on the cost of each kind of mistake.

The one I can't find data for is the false positive — a real customer leaves an honest review and an automated system hides or removes it.

If that's happened to you: did you notice, and how? Did it measurably hit conversion or ranking, or was it just irritating? How long until things recovered? Was there any route to get it reinstated?

Someone told me the impact depends heavily on total review volume — a store with 40 reviews feels a removal far more than one with 4,000. I'd like to know if that matches your experience, because right now my cost model rests on a single conversation.

I am building an automated review moderation agent that classifies new reviews using only the text and star rating. Since the true state (genuine vs. fake/sabotage) is hidden, the agent has to make decisions under uncertainty.

From a store owner's perspective:

1. Which mistake hurts your business more: a False Positive (the agent incorrectly hides a real customer's 5-star review) or a False Negative (the agent fails to hide a fake 1-star competitor review)?
2. What threshold of uncertainty should trigger sending a review to a human manager ("warn/review queue") rather than taking auto-action?

I’m working on a project to build an automated review moderation tool. I want to tune it specifically for niche material suppliers (like those selling custom woven labels, silk fabric, or specialized beads/threads).

Since the AI will inevitably make mistakes when judging if a review is real or spam, I need to know which mistake is worse for your business:

1. A False Positive: The system accidentally hides a glowing 5-star review from a real customer.
2. A False Negative: The system fails to catch a fake 1-star review (maybe from a competitor) and leaves it up on your listing.

Which of these would you pay more to avoid, and why? At what point would you want the AI to just flag a suspicious review for you to check manually, rather than hiding it automatically?

I am designing an automated review moderation system for online stores (focusing on products like craft supplies, apparel, and specialized goods). The agent only has access to the review text and the star rating to decide whether to publish, flag for review, hide, or report a submission.

When a review looks suspicious (e.g., overly generic praise like "Great item best ever" or a harsh 1-star critique with zero detail), what is your standard operating procedure?

1. Do you prefer having questionable reviews published immediately and checked later, or held in a pending queue where they don't show up until approved?
2. How much delay in publishing a real review is acceptable to customers before it hurts trust?

I am modeling a 4-action utility-based agent for product review moderation with the following action space: permit, warn, hide, and report.

In typical Trust & Safety architectures:

1. How is the "warn" action usually operationalized for user-submitted content? Does it mean appending a public uncertainty tag to the post, or routing it to a human review queue?
2. What criteria or probability threshold triggers an immediate hard action (hide/report) versus an escalation to manual review?

I am working on an AI moderation agent tasked with detecting deceptive opinion spam and competitor sabotage given only two inputs: the raw review text and the 1–5 star rating (no user history, IP, or purchase verification data).

I am running into two common edge cases in text classification:

1. Ultra-short text: A 5-star review that just says "ok" or "fast." It lacks psycholinguistic signals of deception but mimics bot activity.
2. Sarcasm / Irony: 1-star reviews written with positive words ("Amazing how quickly this fell apart!").

What NLP feature extraction or prompting techniques have you found most effective for distinguishing genuine short/sarcastic reviews from actual spam when contextual metadata is unavailable?

I’m working on a project to build an automated review moderation tool. I want to tune it specifically for niche material suppliers (like those selling custom woven labels, silk fabric, or specialized beads/threads).

Since the AI will inevitably make mistakes when judging if a review is real or spam, I need to know which mistake is worse for your business:

1. A False Positive: The system accidentally hides a glowing 5-star review from a real customer.
2. A False Negative: The system fails to catch a fake 1-star review (maybe from a competitor) and leaves it up on your listing.

Which of these would you pay more to avoid, and why? At what point would you want the AI to just flag a suspicious review for you to check manually, rather than hiding it automatically?

I’m working on a project to build an automated review moderation tool. I want to tune it specifically for niche material suppliers (like those selling custom woven labels, silk fabric, or specialized beads/threads).

Since the AI will inevitably make mistakes when judging if a review is real or spam, I need to know which mistake is worse for your business:

1. A False Positive: The system accidentally hides a glowing 5-star review from a real customer.
2. A False Negative: The system fails to catch a fake 1-star review (maybe from a competitor) and leaves it up on your listing.

Which of these would you pay more to avoid, and why? At what point would you want the AI to just flag a suspicious review for you to check manually, rather than hiding it automatically?