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No, you’re pretty much stating you created a tool to detect trolling better than any tech company has and you’re using it on me.
Or you’re just drawing random graphs.
You’re trying to say 50% of my comments are accusing people of arguing in bad faith?
I think I’d test the model if I were you.
Sorry I should have been more clear. That was for the "high confidence that the conversation is around Biden" cohort of comments. So within a subset of about 5% of your overall number of comments., so maybe 2.5 - 5% of comments in total you are making one of these kinds of accusations, or about 1:20 or 1:40. I ran a frequency analysis, and at several points you just spam the same comment over and over again, so that might be skewing things. I'm not sure that should be filtered out, because it is trolling.
And yes, I think more testing is required, but most importantly, I think I need to get more of a context window around comments. I want to do this using the whole comment chain or thread. That gets more complicated because now you have 'identities' (speaker A, speaker B, C.. etc), which is where the graphical approach is going to show its benefits. Again, work for another time. At least at a first pass, a few minutes of work adjacent to some other work I'm doing level of effort, its more than sufficient to make my point.
Ok.
I’d be selling it to Google, Facebook or Reddit for many millions but keep using your amazing moderation tool on me buddy.
I’m curious what would happen if you use it on UniversalMonk?
But I almost guarantee there’s a reason why you can’t or won’t.
I'm putting dinner together but I'd be happy to run UM if you would like me to. In exchange would you read the paper so you can understand how the sentiment analysis works? Its important for hypothesis testing. You need to set up good hypotheses for this to be effective.I'm going to down load their comment database now. You work on coming up with some hypotheses.
Oh, that’s the reason you won’t, right?
Because I’m not a computer scientist so I can’t understand the sentiment analysis and come up with appropriate hypotheses?
You were able to for me so why aren’t you able to again?
The fuck are you talking about. I literally said I was doing it in my response.
Bruh this is why you come up as a troll in so much of your comments.
Maybe work on your reading comprehension.
I’m glad you proved my point that you are unwilling or unable to perform the same analysis that you claimed you did on me.
I'm literally setting up for that right now, and for the third time you are accusing of not doing exactly what I'm trying to do for you. It takes a while to download all the comments. I'll let you know when I have them.
That’s the part you might be having troubles with.
I’ve tried telling you that a couple times now.
You were able to do it for me, so there’s no reason you can’t for someone else.
Did you open the paper and read it? The hypothesis are very simple.
They need to be set up with two parts, the first a predicate, then the second part is a couple options..
So for example a hypothesis can be set up in two parts as follows:
Part A:
"The author of this comment { } about a border wall"
Part B:
["thinks negatively" | "thinks positively" "is neutral"]
The options are intended to fill in the gap in the curly braces.
The model will give a probabilistic ranking of the three options, so you need to think carefully about how you set up your hypothesis.
Like I said drop them here or dm me and I can run them once I've scrapped UMs comments.
[Edit: I've got UM's comments, and I've saved them to disk. Let me know if you've got your questions ready, or if you still need help understanding how to set up a hypothesis]
[Addendum] @[email protected]
I'm going to give you a worked example.
This is on UM's most recent comment:
So I set up the predicate:
'The author of this post {} Joe Biden.'
with the options:
['supports', 'opposes', 'is not talking about']
and we get the result:
{'sequence': 'She was on my ballot, so she is a candidate. I don’t know how to explain this any better.',
'labels': ['is not talking about', 'supports', 'opposes'],
'scores': [0.9906510710716248, 0.008063388988375664, 0.0012855551904067397]}
So this comment we would score as "not talking about Joe Biden". Anything you can think of that can fit within that framework. I dont know UM, but you seem to, so you probably know what would be interesting to ask.