- cross-posted to:
- reddit@lemmy.world
- cross-posted to:
- reddit@lemmy.world
Politically-engaged Redditors tend to be more toxic – even in non-political subreddits::A new study links partisan activity on the Internet to widespread online toxicity, revealing that politically-engaged users exhibit uncivil behavior even in non-political discussions. The findings are based on an analysis of hundreds of millions of comments from over 6.3 million Reddit users.
Really curious about the tool they used to quantify “toxicity/disruptive” comments. My initial suspicion would be that political commentary, regardless of human-perceived toxicity, might be biased toward “toxic” by an automated sentiment analysis.
In short: I am suspicious that automated tooling exists to reliably distinguish between toxic and non-toxic political discourse.
We also have to deal with the fact that toxicity has become an almost meaningless label. The way we seem to apply it now, feels like we’d say there was a lot of “toxicity” around the time of the Civil Rights Movement, too. Or even the Civil War.
We’ve conflated “angry, hateful, bitter, disruptive, belittling” with “caring enough to get upset”. There’s been study after study trying to blame social media for the rise in “political toxicity”, and every last single one of them seems to want to sweetly ignore the context of the moment in time we’re living in.
People are acting volatile because there are a lot of volatile events happening that directly affect people’s lives. And all these high-minded discussions about how people online are so mean and rude, or how people don’t listen to each other anymore, consistently sidestep that very crucial piece of context.
So I ask, what do we mean by “toxic”? Because I have a strong feeling a good deal of women were being real “toxic” on June 24 2022. Why is the story not about why? And why does that deserve to be grouped in with the same toxicity comes from the people responsible?
Didn’t check for their specific approach, but this is a pretty standard metric in research.
It mostly boils down to either full mechanical turk (crowd source people to mark whether a post is positive or negative) or generating training data through one. I think there is a Michael Reeves video where he demonstrated this while analyzing /r/wallstreetbets posts since he needed to fully understand all the jargon/stupidity. But the idea is the same. You use humans to periodically update what words/phrases are negative and positive and then have a model train on those to extrapolate.
But there are plenty of training sets and even models out there for interpeting. The lesser ones will see “asshole” and assume negative and “awesome” and assume positive. But all the ones worth using at this point will use NLP to understand that “My asshole itches” is not a negative comment but “It would be awesome if you played in traffic” is very negative.
Also, I am realizing “mechanical turk” sounds like it probably is rooted in racism. Quick google doesn’t make it seem like it currently is, but apologies if that offends anyone and would love an alternative term.
I did read the source, and they’re using a Google AI classifier product, “perspective AI”, and even in the description of the product, it raised questions about its suitability.
At this point, most people in the space are pretty comfortable with the idea that AI models don’t eliminate bias, in fact it can amplify it.
I’m not saying “there is no way to attempt to measure toxicity”, just that based on the specific design of this study, if the measure of toxicity was biased against ANY political discussion, that would be an alternative explanation to the results.
You should read the article, if not the study itself. Its design smells suspiciously like that of an honours thesis as opposed to a grad project. Not just because of the AI… Mostly by the way they defined what constitutes participating in political discussion.
I mean, from a quick test of Perspective using their web page, it is not flagging some pretty strong political statements (mentions of late stage capitalism, calling republicans fascists, accusing Democrats of turning the country into a communist nanny state, etc) and none of them are getting flagged. Whereas, if I tell that text prompt to “go fuck your mother”, it understands that is toxic.
Because… this is kind of a solved problem. There are inherent biases but the goal of this is not to figure out which black man we can frame for a crime. It is to handle moderation. And overly strict moderation means less money. So while there likely is a bias, it does not seem to be an overly strong one and probably actually reflects the perceived reality.
Honestly? It sounds like you don’t like the outcome so you are effectively saying “fake news”.
You must understand the irony in me warning about being careful about drawing conclusions, and you arriving at this conclusion.
What about the outcome would I even find objectionable? The outcome didn’t find a difference between right and left? I DO personally believe that political discourse has gotten extremely toxic. I DO personally believe that people who are politically active ARE in generally more toxic in general conversation. Every single thing in this article confirms what I already believe to be true
I STILL DO NOT LIKE THE STUDY, because I do not believe that the design results in data that necessarily supports the conclusion. I’m not going to give this study a hall pass on rigor because I agree with its conclusion.
Edit:
Also, on the topic of politics and Perspective AI:
Baseline Sentence: “No X could ever be as good a X as Y” Base values: X=CEO Y=Henry Ford
Test Sentence 1: X = CEO Y=Donald Trump +41% more likely to be toxic than baseline
Test Sentence 2: X = CEO Y=Joe Biden +37% more likely to be toxic than baseline
Test Sentence 3: Y = President Y=Henry Ford +61% more likely to be toxic than baseline
Test Sentence 4: X = President Y = Joe Biden +94% more likely to be toxic than baseline
Test Sentence 5: X = President Y = Donald Trump +102% more likely to be toxic than baseline
I gotta be honest with you: my results do not disprove my hypothesis that the system is intrinsically biased to skew any political sentences along the “Toxic” axis