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Earlier quoted context omitted.
Precisely. The cyber stalking of applicants is about to get even worse. Anyone having a public presence on social media is hurting themselves.
And anyone without one is then nobody.
I'd say very little good can come from this. What would be an example of an "appropriate" use of this technology? Basically where it could excel is giving probabilistic prediction of "mental disorders" across a high volume of users. What is a good thing you would do with that? Advertising to suggest someone to get help, maybe?
Our research group applied this tech to Belgium's suicide prevention center chat tool, so the workers can prioritize based on severity.
I'd say very little good can come from this. What would be an example of an "appropriate" use of this technology? Basically where it could excel is giving probabilistic prediction of "mental disorders" across a high volume of users. What is a good thing you would do with that? Advertising to suggest someone to get help, maybe?
Applying it to the statements of dictators so you know when to replace them?
Let’s be honest here… Reddit was their first choice because of the high prevalence of mental disorders amongst its users. One wonders why they didn’t choose Twitter instead in that case… but better to test the AI system’s load capacity before tackling such a gargantuan task I suppose.
> Reddit, which offers a massive network of user forums, was their platform of choice because it has nearly half a billion active users who discuss a wide range of topics. Let’s be honest here… Reddit was their first choice because of the high prevalence of mental disorders amongst its users. One wonders why they didn’t choose Twitter instead in that case… but better to test the AI system’s load capacity before tackl…
I don't know, but I doubt it's that Reddit users are substantially less mentally healthy than Twitter users. There might be a mean intelligence difference (just guessing), but Twitter isn't exactly known for rational and sane communication of ideas.
Although might Reddit simply have a larger corpus per user given the lack of a character limit?
EDIT: The more I think about it, the more I think my assumption is close to the truth. Even if your character limit is 280 characters, that limit can affect how someone forms their sentences. If there is no meaningful limit then a person can feel they can express their full idiolect unencumbered.
I'd say very little good can come from this. What would be an example of an "appropriate" use of this technology? Basically where it could excel is giving probabilistic prediction of "mental disorders" across a high volume of users. What is a good thing you would do with that? Advertising to suggest someone to get help, maybe?
> What would be an example of an "appropriate" use of this technology? Applying it to the statements of dictators so you know when to replace them?
I'd say very little good can come from this. What would be an example of an "appropriate" use of this technology? Basically where it could excel is giving probabilistic prediction of "mental disorders" across a high volume of users. What is a good thing you would do with that? Advertising to suggest someone to get help, maybe?
Youre right that getting rates of mental disorders at the population level is a useful application. Checkout for example the hedonometer: https://hedonometer.org/timeseries/en_all/?from=2021-12-05&t... . People have also done population-level research showing that bad weather affect various seniment-based metrics of mental health: https://journals.plos.org/plosone/article?id=10.1371/journal...
1) The oversampling of the training set makes it uncalibrated when applied to the general population (to recalibrate you need an estimate of the prevalence to begin with, which sort of defeats the purpose).
2) Online posts are not a random sample of the population. (Perhaps this is solveable with some poststratification of the estimates, although requires demographic data on the poster.) If you take self reports that the researchers used to define disorders at face value, those would make more sense than using this model.
These text based models are so superficial, when applied to mass datasets with low prevalence of the underlying condition, they will ultimately result in very low positive predictive values (e.g. flag 100 people, if the model is good will only get 5/100 as actual mental health problems).
As version_five asks, it is hard to imagine any reasonable use of the model given such low positive predictive values (which imply incredibly high false positive rates).
Hopefully you won't pop up on an AI definition of a mental disorder.