I agree that most blogs, particularly the “successful” ones are mostly self righteous, puffed up, pseudoscience, pontificating tosh.
Most of the authentic stuff doesn’t see the light of day
RE: Natural Language Text Analysis of most Up voted and Down voted posts in December 2017
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Natural Language Text Analysis of most Up voted and Down voted posts in December 2017
"pontificating tosh" hahah nice
It's a shame that the only people who care and are willing to make efforts are also the people that spends hours writing the quality posts. So time consumed. Whilst the self righteous and spammers will always be there to abuse a system and quickly in numbers. Still racking brainz on ideas of how to improve... the tagging words are interesting though, giving me things to think about. I like the psychology behind it. :)
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I think auto-tagging might be viable. Where an algorithm looks at word clusters and associations then assigns a tag... would prevent tag spamming
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Sounds like a good idea! ... How easy would it be to implement though? I definitely think the tagging angle is one of the prime solutions... really hard to think of much else that doesn't require lots of monitoring and efforts from individuals.
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Many already exist and public domain. Data scientists use them and pull them down from CRAN. The heavy math is already done, it’s an integration job... would need to be hard wired into the steemit platform though. Not something optional that users opt-in to.
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(I just had to google what CRAN is... im guessing you mean the programming verison but maan there's a bunch of choices... "a measurement of uncleaned herring" :D
Is there anyone who is in charge of full platform changes or is it all spread out across users do you know? ... I'd like to think that people have already made plans to stop greedy spamheads. Always interesting to see what evolves :D
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I’m too new around here to know but I imagine steemit Inc. Keeps tight control of the product roadmap.
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Eye... in the meantime we just keep playing and giggling innit! :D
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