RE: Opening the Kimono of Onboarding Data | Time for Full Transparency

Hey Ura, thanks for taking the time to drop by and leave your feedback. Thank you for all the feedback as well as it helped guide the development of this dashboard.

As I mentioned in our DMs, the key factor for me here is not so much the data and numbers themselves, but the ability to verify that the new users are real people who bring some kind of value to the chain. One of the primary goals of digital marketing is to not only bring in traffic, but to attempt to bring in targeted and QUALIFIED traffic, that is likely to bring value and to convert into some kind of behaviour that achieves the goals of the organisation.

I fully agree with this. The way we parse the data we're retrieving for the dashboard has some filtering when we parse for "Real Creators"

For example, here on the MACs trend, you can see ~2120 accounts were removed from the data as we found them to be AI generated/operated (or at least appeared to be).

The way we did that is by parsing the data with specific filters. We've created a handful of filters like these - and as AI gets smarter, we will need to continually tweak our filters. Hive is no stranger to people trying to game the system, even for pennies worth of rewards. Our goal is to filter out as much of that as we are capable through both automated and manual tactics. A few of the automated filters:

Time between comments - bots/AI tend to have very specific and rigid standards. If an account is posting every 7 minutes on the dot, then we are able to flag it as a potential bot and then remove it from our data
Time from sign up to first post - some of these bots have abnormal abilities to make their first post/thread within seconds of signing up. We've found a way to filter those out as well
Profile image - many of these AI accounts either have no profile image or have an AI generated one. We can easily filter out no profile image but the AI generated ones are a bit harder to flag. We've found some ways to flag them as "potential AI" then see if another part of our filtering flags them for something else
...ongoing. As all of this evolves (both the tools from AI and the tools on our end), we have to keep evolving our filters. I didn't list them all here as part of the game theory is that the real people who are making these AI accounts may be looking for all the exact filters in order to game the system. We'll continue to evolve our methodologies. The Dashboard released as part of this post will continue to flag and remove AI accounts from the data both manually and automated based on our filtering

I invite anyone to download the raw data and confirm our filtering. If anyone has suggestions on how to filter better after doing so, I am all ears and will implement more filtering mechanics.

Providing the full data dump should hopefully be sufficient to allow the community to do whatever due diligence they prefer with regard to clarifying the situation for themselves

I fully agree here. After speaking with you and others, I realized the best way to respond to this is to build & release this dashboard and also the raw data that is pulled onchain to display the data.

We use the #newlions tag and a few other tags to filter for new accounts created. We also have a referral tracking system that helps users see who was onboarded from their links (i.e. if you share a blog post link, it attaches your referral ID and if anyone signs up from it within 30 days, they are attached to your account and you can view them in your Referral Dashboard on INLEO). We modeled this after Amazon's Affiliate program (30 day cookie dropped in the link clicker's browser. If they sign up within 30 days, the original referrer gets credit).

Quality testing is absolutely essential. I 100% agree. We will continue to work hard to filter people out, parse data better and have both automated and manual ways of finding the humans. Thank you for all your feedback 🦁