u/parteeksaini

I have decided to make a probabilistic model which finds out weather you should buy a particular crypto or not. i have researched about crypto and found that three things are important and in those three things there are certain patterns which can be seen.
The first is Momentum of crypto(strong, neutral, weak), Fundamentals(how the company is growing(strong, neutral, weak), market(adverse, neutral, bullish)
so my agent see's the evidences and then based on the past data pull out the base rate as first belief distribution among 5 hidden states,

1. strong upward trend
2. weak upward trend
3. sideways
4. strong downward trend
5. weak downward trend

so the probability will be distributed among these from base rate from past data. so thee base rate will work as prior and then based on the specific patterns the agent will go inside the data see the specifications and calculate the numbers among all of the hidden states, find the probability of each happening by applying bayes rule and then by seeing a certain threshold and based on the events it will decide what to do, buy or sell

guys which signs usually tells you that okay we need to buy this crypto and which signs usually indicates that we shouldn't buy this?

I'm working on a probabilistic AI agent for a research project that flags fake e-commerce reviews

Here is how the pipeline flows:

When a review comes in, it hits Level 1 first. This is a fast pass (under 50ms) that evaluates quick metadata like verified purchase status, account age, 24-hour review frequency, and text length heuristics. It runs a Naive Bayes update against historical base rates to get an initial probability that the review is genuine.

If P(Genuine) is over 60%, the review is auto-approved. If it drops below 20%, it routes to a high-priority human ban queue (the agent never auto-bans accounts on its own).

If the probability lands in the gray zone between 20% and 60%, it triggers Level 2.

Level 2 is a deep check. It pulls the user's past 5 to 10 reviews, runs vector embeddings to measure cross-review similarity to catch copy-paste templates, and checks their brand concentration ratio (how many of their total reviews target a single seller). It calculates a secondary Bayesian update using the Level 1 score as the prior.

If the updated score passes 60%, it approves. If it drops below 20%, it goes to the high-priority ban queue. If it remains stuck between 20% and 60%, it goes to a separate "unclear review" human queue where a human looks at it without taking any automated penalty.

My goal is to keep human reviewers in the loop while splitting suspected bot spam from messy/blunt genuine reviews so moderators don't burn out from context-switching.

so to categorized text length into some cases like short(use case written or not) and then changing it into a number in a json format which open source model will work the most efficiently?

If a business is working on fixing their fake reviews and detecting them by implementing a fake review finder agent and there are potentially 2 mistakes that can happen either you can send a particular id of a real user to ban list where human have to review it(will take time and expensive) or approve it even though it was a fake review, which mistake is potentially more harming and if you have to give your agent a percentage if the probability of being that review is genuine is more than a certain percent than then approve it either not what will you chose?

I'm currently researching probabilistic moderation agents for catching fake e-commerce reviews for a project.

One challenge I'm trying to model is how bad actors adapt when they realize how automated detection works. In my setup, the agent uses fixed probability thresholds and likelihood ratios for signals like account age, review frequency, and text similarity. If a review drops below a 20% probability of being genuine, it gets flagged for high-priority human ban review.

The issue is that bad actors quickly figure out where these boundaries are. Once they realize that posting 5 reviews a day triggers a high-frequency signal, they throttle back to 1 review every few days. Or they buy cheap digital items just to get a verified purchase badge and artificially boost their Level 1 score.

For anyone who has worked on content moderation or fraud systems, How do you handle threshold gaming and signal decay in practice? Do you dynamically adjust your likelihood estimates as fraud patterns shift, or do you rely on periodic manual recalibration of your base rates?

I’m currently building a probabilistic AI agent to detect fake reviews for a project, looking at signals like account age, posting frequency, and brand concentration.

Positive fake reviews usually leave fairly clear statistical footprints (high frequency, unverified purchases, single-seller focus). However, competitor negative review attacks seem much harder to catch automatically because bad actors often buy cheap items to get "Verified Purchase" badges or use aged accounts to bypass standard filters.

For anyone who has dealt with listing attacks: What subtle patterns or behaviors usually give away a targeted negative review attack versus a genuinely unhappy customer? Is there a specific signal you’ve noticed that automated filters usually miss?

I'm working on a probabilistic AI agent for a research project that flags fake e-commerce reviews, and I'd love to get a sanity check on the overall architecture from folks who have built similar moderation pipelines.

Here is how the pipeline flows:

When a review comes in, it hits Level 1 first. This is a fast pass (under 50ms) that evaluates quick metadata like verified purchase status, account age, 24-hour review frequency, and text length heuristics. It runs a Naive Bayes update against historical base rates to get an initial probability that the review is genuine.

If P(Genuine) is over 60%, the review is auto-approved. If it drops below 20%, it routes to a high-priority human ban queue (the agent never auto-bans accounts on its own).

If the probability lands in the gray zone between 20% and 60%, it triggers Level 2.

Level 2 is a deep check. It pulls the user's past 5 to 10 reviews, runs vector embeddings to measure cross-review similarity to catch copy-paste templates, and checks their brand concentration ratio (how many of their total reviews target a single seller). It calculates a secondary Bayesian update using the Level 1 score as the prior.

If the updated score passes 60%, it approves. If it drops below 20%, it goes to the high-priority ban queue. If it remains stuck between 20% and 60%, it goes to a separate "unclear review" human queue where a human looks at it without taking any automated penalty.

My goal is to keep human reviewers in the loop while splitting suspected bot spam from messy/blunt genuine reviews so moderators don't burn out from context-switching.

so give me your honest reviews and actually where it can fail.

My architecture runs Level 1 fast metadata checks (account age, verified purchase, review frequency) and falls back to Level 2 text embeddings (cross-review similarity) and brand concentration metrics only when a review lands in an ambiguous "gray zone" (between 20%-60%) but When a gray-zone review comes from a brand-new user with 0 past reviews, Level 2 cannot compute cross-review text similarity or brand concentration due to the complete lack of historical data.
right now my approach is if there's no past history we will send it to human review but it's not cheap so what should i do?

ayo guys, im thinking of building an ai system which is useful for mid-large community based apps, basically it will read the post before anyone posts it and if it found some potential harm it will not let it post, sounds basic right? well i have build the architecture so lets see my effort. Try to destroy me with all of your questions lets see.

So listen guys im going to build an agent for mid-large community based apps, basically it will read the post before getting it posted and if it found potential harm(abusing, harming someone in potential way) its not going to be [ Post --> LLM reviews --> hold or release ] it too basic, i have built an architecture, what do you guys think of it?

guyss, im thinking of building an agent which sees if the content(text) that a user is going to post is dangerous(abusive, harming) it will not let it post it and even report to the management, what do you guys think?