Tim Walz has taken on a leveled-up approach in a race to the finish of the 2024 election, after a more cautious and buttoned-up start as Kamala Harris’ running mate.
In the weeks following the vice presidential debate, Democratic vice presidential nominee Tim Walz has been sounding more like the aggressive campaigner who got the role than the buttoned-up figure he’s cut since joining the ticket.
Dressed in khakis and a navy Harris-Walz sweatshirt Monday, Walz delivered some of his sharpest attacks yet against former President Donald Trump. Walz appeared more natural in his latest appearances on the trail, including in his signature flannel in rural Pennsylvania, after shedding the blue sport coat and white collared shirt he’s favored for the last few months.
He’s also getting back on the TV circuit, with appearances coming up on “The View” and “The Daily Show,” according to a campaign official, after Walz went viral pre-running mate selection with his labeling of the GOP ticket as “weird” in a cable news interview.
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[I had to switch to a larger compute instance]
@SatansMaggotyCumFart@lemmy.world
Ok, results are in. Firstly, just some preliminary stuff on your posting behavior. I only pulled your last 6k comments, which goes back to February. You caught me at a good time, because I had been working on parts of this for a while for some network analyses I’m working on looking at the relationship between moderation bias and community sentiment, so I had some of these tools just laying around.
It looks like you really got posting in around April, and hit your stride over summer. You’ve slowed down a bit since. Also, you tend post most frequently at about 19:00 GMT or 3PM EST/ 12 PST, and then again around midnight GMT, or about 8PM EST/ 5PM PST.
For this work, I’ll be using some models from this paper: https://huggingface.co/papers/2409.02078, “Political DEBATE: Efficient Zero-shot and Few-shot Classifiers for Political Text”. This tool allows me to set up hypotheses like the following.
multilabel entailment labels
The multilabel option determines if more than one hypothesis can be true for the document. If false, the most likely label is returned. If true, a dictionary of labels and their estimated probability is returned.
Below is the result of the hypothesis ‘The author of this reply {} Biden.’, with the two options: {h0: ‘is talking about’, h1: ‘is not talking about’], where we accept h0 at >0.5
It appears that your posts mention Biden at a relatively uniform rate. Please note that we’re in percent of posts, not count (as with the previous two figures), since your post frequency has changed over time. It seems like for any given week, 5-15% of your posts typically mention Biden.
So for the below analysis, I tested the hypothesis “The author of this reply {} Biden.”, with h0 being “is supportive of” and h1 being “is in opposition to”. I only performed this analysis that had a very high probability of being about Biden.
So, “generally” supportive, but not crazy. You started less supportive of Biden than you are now, but like I said, I only grabbed the previous 6k comments of yours. Generally you seem to be about 50/50 on Biden. Which is against my previous assumption, I thought you were more supportive of Biden (closer to 80-90%).
The next experiment I ran was the test (on all of your comments, not just the ones mentioning Biden) was the hypothesis “The author of this reply {} Biden.”, with h0 being “is being abusive, or trolling.” and h1 being “is being honest and genuine.”. I ran this test on all comments.
Honestly, @SatansMaggotyCumFart@SatansMaggotyCumFart@lemmy.world , I think you can up your game. You’ve got ample headroom to live up to your legacy.
HOWEVER… If we look at the same results for posts which are explicitly about Biden… we can see that you are trolling and abusive at a rate much higher than your background rate.
So there is your answer. Not as bad as I thought, but not great. Definitely an abusive troll when it comes to political discussions.
Some limitations about this approach. I want to expand it to include the context that a given comments sits in. Its fine for a cursory analysis like this to just use single comments, but context is key. I think we’ll get much clearer signal/ noise with more context. Also, these conversations happen in a threaded manner. I need to develop a way of accounting for that. I’ll probably pull some methods that I’ve used for network analysis for that component. But I got the major issues out of the way, and I can run these kinds of analysis for anyone on the fediverse. So for a preliminary step, its at least on its way to being sufficient to identify bad faith/ troll accounts.
Gunna be honest here, this just makes you look insane, terminally online, or both. It doesn’t help your position, and just because you can make graphs doesn’t somehow make you any more correct in this context.
Maybe step back and see what you just wasted your time doing. You changed no minds, put in a ton of effort, and for what?
Wow he really got you there, listing number of comments and stuff!!1!
By what basis do you consider a comment abusive or trolling?
Don’t forget this is what you’re trying to prove, and you make a bunch of charts that don’t really prove anything instead.
Its part of how the hypothesis is set up. You can read the paper I cited here: https://arxiv.org/abs/2409.02078
So specifically for that question the hypothesis “The author of this reply {} Biden.”, with h0 being “is being abusive, or trolling.” and h1 being “is being honest and genuine.”
And on your second point, since I’ve still got the data up, we can address that specifically. We’ll address the following hypotheses. ‘The author of this reply {}’: ‘is accusing someone of being a russian asset.’, ‘is accusing some one of trolling.’, ‘is accusing someone of being a bot.’, ‘is accusing someone of engaging in bad faith’, ‘is having a normal conversation’.
Only if you lack reading comprehension.
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.
@satansmaggotycumfart@SatansMaggotyCumFart@lemmy.world
I’m downloading the data and will save it to disk, but am about to sit down to dinner. I’ll save the output and either drop the hypotheses you want to test here or dm me, but I may not respond tonight.