David H.

← Grindr Feature Suggestions

My feedback

3 results found

  1. Mass-select profiles from feed to block (or hide) all at once

    3,982 votes
    174 comments  ·  Grindr Feedback  ·  Admin →
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    David H. commented  · 

    OMG yes! I don't know how this doesn't have more votes LoL. Been waiting for this for as long as I've been using Grindr.

    David H. supported this idea  · 

  2. Taps optional

    3,737 votes
    404 comments  ·  Grindr Feedback  ·  Admin →
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    David H. supported this idea  · 

  3. Have a way to filter out bots solicitation and scams

    24,911 votes
    2811 comments  ·  Grindr Feedback  ·  Admin →
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    David H. commented  · 

    Totally agree on this entire topic. I posted a different feature request with a different solution that would be frictionless to authentic users and better at preventing fake and illegal profiles. Please see it and up vote so Grindr will get it in their backlog!

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    David H. commented  · 

    I see feature requests for account validation / authentication with votes of well over 12k in this forum alone. This is clearly the utmost importance to your users.

    I believe we have a lot of low hanging fruit to weed out the noise accounts (fake, spam, solicitation, dangerous, etc.) leaving a grid users can count on as clean, real, accurately represented people and have confidence that Grindr support has the tools necessary to step in and help when necessary.

    That being said, I recommend the use of MAC and IP addresses to help improve the experience around this issue. Phone and email are easily created for free and are small points of friction for those creating fake or fraudulent accounts. They also offer little to no traceability for enforcement of legal action where necessary.

    While IP addresses may be proxied, requiring exposure (to get around VPN protections) will at least help authorities trace the IP to ISP of origin and take appropriate action from there.

    MAC addresses do not currently have a way to be altered or proxied. If Grindr requires access / systematic disclosure of each device that logs into the platform and associates it [them] with the IPs and email addresses, this creates a significantly stronger reliability of authentication / know your customer chain. I would suggest MAC be [part of] an account's UPK. At minimum, this would require banned people to purchase new devices if they intend to create new accounts before their ban expires, something I believe to be a large and costly deterant. This would also require that virtual devices may not be used to access the Grindr platform in production. Being a systematic step, user experience should not be impacted except in cases where they attempt to block their MAC and/or IP. Little to no new friction.

    I think this is the best option we currently have to limit banned people from re-entering the platform. Equally as important, impersonation where nudity is involved and/or a minor is illegal in many locations. MAC plus IP would be a significant help to authorities to track down the person behind the account for appropriate action.

    Additionally, this does provide broader user analytics in the form of a user view across all their accounts, devices, and access points / locations - better targeted marketing (and more) for you :-)

    Another feature request of importance in the same domain of topic: I have become a pro at spotting fake accounts at a quick glance. If a human can do that, a machine can do...often better. I recommend implementation of ML to both identify potential fake / impersonation accounts and accounts not compliant with legal [EULA and T&Cs] conditions. Use cases include a basic MVP of accounts where words have capital Ts not as the first letter of a word which most often represents the solicitation or elicitation of "Tina" aka crystal methamphetamine, links to other sites for **** spam, the word "purchase"; to more advanced cases such as integrating with Google's image search API to identify potential celebrity / well-known persons being impersonated in the account before they go live and many, many more cases. Combining ML and the user reports to feed into it's nueral network for new pattern potentials work collaboratively for a significantly more powerful and efficient solution. Identified potentials could then be submitted for review by your operational staff and flow downstream in the existing process for action.

    David H. supported this idea  · 

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