Treating "highest ranked," "most upvoted," "most popular," and "frequently cited" as a signal of quality or authoritativeness has proven to be a persistent problem for decades.
Depends on the metric. Humans who up-voted that material clearly thought it was worth.
The problem is distinguishing the various reasons people think something is worth and using the right context.
That requires a lot of intelligence.
The fact that modern language models are able to model sentiment and sarcasm as well as they do is a remarkable achievement.
Sure there is a lot of work to be done to improve that, especially at scale and in products where humans are expecting something more than a good statistical "success rate", but they actually expect the precision level they are used from professionally curated human sources.
In this case it was a loss of context.
The original post was highly upvoted because in the context of jokes it was considered good.
Take it out of that context and treat "most upvoted" as a signal that means something like authoritativeness and the result will be still be hilarious,
but this time unintentionally so.
