@steventhedev - eviltoast
  • 44 Posts
  • 403 Comments
Joined 1 year ago
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Cake day: June 23rd, 2023

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  • This is a mentally ill person who was driven to an extreme and felt there was nothing better to do than take his own life.

    There is no message that should be said other than to urge anyone who is feeling similar distress needs to know that there are people who love them and no matter what there is always a better alternative.

    By condoning it for political purposes you give an out for the mentally ill to commit “legitimate” suicide, or worse to being manipulated into doing so. This is not a slippert slope, it is a hard line that many in these comments have crossed - which is why it needs to be said that there is a better path and there are resources.











  • Headlines are sampled randomly for the first few hours of an article going live to measure exposure. The headline that gets the most clicks wins.

    There are a lot of sites that do this.

    It causes headaches when it comes to social. Usually the original headline is preserved in the url, but sometimes they’ll use a unique id and then include the editorialized headline option so they can track which headline you clicked on.

    Also editorial decisions on wording based on pushback, legal threats, etc.





  • steventhedev@lemmy.worldtoNews@lemmy.world[META] MBFC bot
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    16 days ago

    That’s…actually not a bad idea. Take the user-domain name pairs and weigh the edges between domains by the number of unique users who posted from both domains.

    For producing clusters from the resulting graph should be easy, but aside from just saying “these are similar websites” does it really say much?

    You could do something similar with comment/upvote/downvote based linkages - maybe they’ll have some deeper semantic meaning



  • steventhedev@lemmy.worldtoNews@lemmy.world[META] MBFC bot
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    18 days ago

    I don’t see an easy way to accomplish this without either pulling in the full text of every article over some period and running something like paragraph/doc/site vectors and then clustering by site vector.

    That’s putting a lot of faith into unsupervised learning, and it’s probably just as likely to pick up on stylistic conventions like byline and date formats as it is to cluster by some common thematic pattern like political leaning.