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Comment by Barrin92 | Hacker News Reader

>"The work centers on the way topics are addressed with particular phrasing, such as undocumented immigrant | illegal Immigrant, fetus | unborn baby, demonstrators | anarchists."

This to me is phrenology 2.0 or something. Not only does this have absurd explicit shortfalls, like the fact that words like 'anarchist' can be derogatory or sympathetic purely depending on context on opposite sides of the spectrum, this completely ignores any nuance, sarcasm, or anything else that really reveals bias beyond the obvious.

I'd suggest sampling a thousand random people, having them rate everything on a spectrum and average it out, or consulting the title of the publication. Saves you the compute. We don't need ML to determine that the American Conservative, is surprisingly enough a right-wing publication

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The word anarchist is a bad example but for undocumented immigrant vs illegal immigrant or fetus vs unborn baby, it's utterly clear what the connotations are.

> this completely ignores any nuance, sarcasm, or anything else that really reveals bias beyond the obvious.

This problem is mentioned several times in the article, and several ways it manifests itself is discussed.

but no real solution is offered to rectify it, and if you have such glaring problems it basically renders the thing useless in any scientific sense.

I'm focusing on it because of the implication spelled out at the end:

"Nonetheless, the MIT study seems to be the largest of its type to date, and could form the framework for future classification systems, and even secondary technologies such as browser plug-ins that might alert casual readers to the political color of the publication they are currently reading."

What's happening in that study is not science but alchemy, a gigantic problem in the field. Entire sectors are now infested with this stuff, see 'affective computing' for another offender.

Yes, and it isn't alchemy, since no gold is created.

It is tautology.

It's experimental, you just use it or not depending on its usefulness.

> that might alert casual readers to the political color of the publication they are currently reading

So many false positives with ESL writers.

Phrenology is a fake science finding patterns where none exist. But human language and political thinking are deeply intertwined. You’re right that you don’t need ML to know a certain conservative outlet is right wing, but automated analysis can help show us the full spectrum of coverage for a given issue, and help show the more open minded consumers how their particular media compares to others. There’s a lot we can learn by studying how people use language.

The point is that language is not enough. I'm a fierce critic of how contemporary media seems to be moving away from NPOV as a default, but objectivity requires, at a minimum, an attempt to properly weigh and prioritize information. Which is deeply contextual and even time-sensitive.

The usual example is an hypothetical Wikipedia page starting with the sentence "Adolph Hitler was a soldier, a painter and a statesman". It's not wrong, but I wouldn't say it's a good summary either.

But…. They picked up some signal, and the examples they give are both correlated with each other, and with our intuition? So what “is” that signal, and if it correlates well with the left-right dimension, what does it matter if it’s not exactly the same, or not causal in any direction?

All I can think of is that using this data to judge biases could be defeated by a simple search-and-replace if anyone wanted to. But I doubt publishers cate enough about obscure papers calling them conservative to change their language. (If they do stop calling all foreigners “illegal aliens”, that’s would actually be quite the feat for a lowly department of semantics)

It can be defeated by engaging in sarcasm. It can be defeated by using right-wing language to construct a left wing argument (one of the most straight-forward ways to persuade someone is to speak their language), one can twist an argument entirely by minor adjustments to speech.

The issue here is a simple one, that in communication, language cannot be divorced from context. Laughing at a birthday party means you're having fun. Laughing at a funeral means you're distressed. Laughing itself has no meaning, and words themselves have no meaning. You don't 'pick up' signals, you interpret something as a signal within a frame of reference.

This methodology is right in every case where it is trivial, and thus unnecessary, it is likely wrong in every case where it matters. And that is an actively bad combination.

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