I think the simplest way to explain it is that the average person isn’t very skilled at rhetoric. They argue inelegantly. Over a long time of talking online, you get used to talking with people and seeing how they respond to different rhetorical strategies.
In these bot infested social spaces it seems like there are a large number of commenters who just seem to argue way too well and also deploy a huge amount of fallacies. This could be explained, individually, by a person who is simply choosing to argue in bad faith; but, in these online spaces there seem to be too many commenters who seem to deploy these tactics compared to the baseline that I’ve established in my decades of talking to people online.
In addition, what you see in some of these spaces are commenters who seem to have a very structured way of arguing. Like they’ve picked your comment apart into bullet points and then selected arguments against each point which are technically on topic but misleading in a way.
I’ll admit that this is all very subjective. It’s entirely based on my perception and noticing patterns that may or may not exist. This is exactly why we need research on the topic, like in the OP, so that we can create effective and objective metrics for tracking this.
For example, if you could somehow measure how many good faith comments vs how many fallacy-laden comments in a given community there would likely be a ratio that is normal (i.e. there are 10 people who are bad at arguing for every 1 person who is good at arguing and, of those skilled arguers 10% of them are commenting in bad faith and using fallacies) and you could compare this ratio to various online topics to discover the ones that appear to be botted.
That way you could objectively say that on the topic of Gun Control on this one specific subreddit we’re seeing an elevated ratio of bad faith:good faith scoring commenters and, therefore, we know that this topic/subreddit is being actively LLM botted. This information could be used to deploy anti-bot counter measures (captchas, for example).
Would you mind elaborating? I’m naive and don’t really know what to look for…
I think the simplest way to explain it is that the average person isn’t very skilled at rhetoric. They argue inelegantly. Over a long time of talking online, you get used to talking with people and seeing how they respond to different rhetorical strategies.
In these bot infested social spaces it seems like there are a large number of commenters who just seem to argue way too well and also deploy a huge amount of fallacies. This could be explained, individually, by a person who is simply choosing to argue in bad faith; but, in these online spaces there seem to be too many commenters who seem to deploy these tactics compared to the baseline that I’ve established in my decades of talking to people online.
In addition, what you see in some of these spaces are commenters who seem to have a very structured way of arguing. Like they’ve picked your comment apart into bullet points and then selected arguments against each point which are technically on topic but misleading in a way.
I’ll admit that this is all very subjective. It’s entirely based on my perception and noticing patterns that may or may not exist. This is exactly why we need research on the topic, like in the OP, so that we can create effective and objective metrics for tracking this.
For example, if you could somehow measure how many good faith comments vs how many fallacy-laden comments in a given community there would likely be a ratio that is normal (i.e. there are 10 people who are bad at arguing for every 1 person who is good at arguing and, of those skilled arguers 10% of them are commenting in bad faith and using fallacies) and you could compare this ratio to various online topics to discover the ones that appear to be botted.
That way you could objectively say that on the topic of Gun Control on this one specific subreddit we’re seeing an elevated ratio of bad faith:good faith scoring commenters and, therefore, we know that this topic/subreddit is being actively LLM botted. This information could be used to deploy anti-bot counter measures (captchas, for example).