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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    • @TropicalDingdong@lemmy.world
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      1 month ago

      Your trolling says more about you than it does me. I advocated to swap the candidate for literally months because Biden was an absolute TRAINWRECK of a candidate. There was about a 0% chance of Biden winning this election, and if I recall correctly, you defended keeping him as the candidate, in-spite of the incredibly obvious fact that he was losing the election in a dramatic fashion. You defended him to the point of calling any one asking to remove him a trolls, bots, and Russian assets.

      So you weren’t interested in improving the Democrats chances of winning. You were just behaving like an abusive troll. And I called that Biden wouldn’t be the candidate litterally 8 months out. I was right, and literally almost everyone in this forum was wrong.

      And for a few weeks, we had a good thing going with Kamala. But she’s shifted to campaigning with neocon’s and being “pro-buisness”, and accordingly, her polling is dropping off a cliff.

      She needs to shift back to how she campaigned/ policies she had before and up until the convention. IF she doesn’t, she will lose this election.

      But you aren’t here for nuance. You just want to pretend that you are on the right side of anything.

      And just as before, you aren’t interested in figuring out what it would take Kamala to win. You are just here to abuse.

      • SatansMaggotyCumFart
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        81 month ago

        You defended him to the point of calling any one asking to remove him a trolls, bots, and Russian assets.

        No.

                  • @TropicalDingdong@lemmy.world
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                    @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.

                    samples = list(test[‘premise’]) template = ‘The author of this reply {} Biden.’

                    multilabel entailment labels

                    labels = [‘is talking about’, ‘is not talking about’]

                    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.

                    res = pipe(samples, labels, hypothesis_template = template, multi_label = False)

                    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.