InfoSec Person | Alt-Account#2

  • 6 Posts
  • 36 Comments
Joined 1 year ago
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Cake day: September 28th, 2023

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  • That seems to be the consensus online. But thanks for that tidbit! It feels even more bizarre now knowing that.

    I wonder why a handful of people think the way I presented in the post. Perhaps American/British influences in certain places? Reading books by british authors and books by american authors at the same time? Feels unlikely.





  • My bachelor’s thesis was about comment amplifying/deamplifying on reddit using Graph Neural Networks (PyTorch-Geometric).

    Essentially: there used to be commenters who would constantly agree / disagree with a particular sentiment, and these would be used to amplify / deamplify opinions, respectively. Using a set of metrics [1], I fed it into a Graph Neural Network (GNN) and it produced reasonably well results back in the day. Since Pytorch-Geomteric has been out, there’s been numerous advancements to GNN research as a whole, and I suspect it would be significantly more developed now.

    Since upvotes are known to the instance administrator (for brevity, not getting into the fediverse aspect of this), and since their email addresses are known too, I believe that these two pieces of information can be accounted for in order to detect patterns. This would lead to much better results.

    In the beginning, such a solution needs to look for patterns first and these patterns need to be flagged as true (bots) or false (users) by the instance administrator - maybe 200 manual flaggings. Afterwards, the GNN could possibly decide to act based on confidence of previous pattern matching.

    This may be an interesting bachelor’s / master’s thesis (or a side project in general) for anyone looking for one. Of course, there’s a lot of nuances I’ve missed. Plus, I haven’t kept up with GNNs in a very long time, so that should be accounted for too.

    Edit: perhaps IP addresses could be used too? That’s one way reddit would detect vote manipulation.

    [1] account age, comment time, comment time difference with parent comment, sentiment agreement/disgareement with parent commenters, number of child comments after an hour, post karma, comment karma, number of comments, number of subreddits participated in, number of posts, and more I can’t remember.













  • I think the difference lies in two things:

    • You can share an article from a user of a different instance. In this case, your instance will have to look up the rel=“author” tag and check whether the URL is a fediverse instance. I’m not sure whether this is scalable as compared to a tag that directly indicates that the author is on the fediverse. Imagining a scenario where there are 100, 1000, 10,000, or 100,000 instances on different versions.

    • The tag is to promote that the author is on the fediverse. If the rel=“author” tag points to twitter for example, maybe Eugen Rochko + team didn’t want a post on the fediverse to link to twitter.

    These are my thoughts and idk if they’re valid. But I think just reusing the rel=“author” isn’t the most elegant solution.

    I know that mastodon already uses rel=“me” for link verification (I use it on mu website + my mastodon account), but that’s a different purpose - that’s more for verification. There’s still no way of guaranteeing that the rel=“author” tag points to a fediverse account. You’re putting the onus on the mastodon instance.