Machine learning is too often perceived as a kind of oracle-like power capable of predicting the unknowable and working out of distribution.
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His proposal is not overly egregious. Predicting the evergreen character of a news article is a common problem and works decently well even with basic techniques (fun fact: I won my first Kaggle competition on exactly this topic in 2013: https://www.kaggle.com/c/stumbleupon/leaderboard …)
What makes his proposal dubious is the notion of training on articles from past decades, which means the classifier would suffer badly from concept drift. The proper setup is actually to create a training dataset by asking people to rate *current* articles as evergreen or not.
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But the “proper setup” would be using a less useful target variable (i.e., article is predicted to be evergreen rather than article actually withstood the test of time). So there’s a tradeoff between these two possible setups, right?
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There's no tradeoff here. You have to understand that such classifiers work not because they have a crystal ball, but because they latch onto superficial markers that are statistically correlated with evergreeness or not (e.g. lots of dates = not evergreen).
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