To;dr: fairly strong and reliable correlation between depression and posting images that are bluer, grayer and darker. This is predictive in that users can be identified as depressed before they are disgnosed. # of faces appearing in user pics was also indicative of depression. Fewer faces per picture correlated with depression. # of comments more weakly correlated with depression, and # of likes was negatively correlated. Mechanical Turk-tasked humans were also able to fairly accurately identify depressed users, but often identified different users than the machine.
Statistical methods: Bayesian feature extraction with uninformed priors. 100-tree random forest for classification.
Some points of caution: depression is a broad, fairly fuzzy term. The authors acknowledge that this complicates matters. Some self-selection bias possible, as users had to provide permission to access Instagram streams and many users opted not to.
This is then compared with GP's rate of success at diagnosing depression. I fear that's a slightly misleading comparison, because the study works with a different pool of patients to be diagnosed than the typical GP. The Instagram methodology solves a different, maybe easier, problem.
You have to go to the Appendix, but it appears that 71 of the sample were depressed (which presumably means that they have answered yes to the depression question). It's not clear if they used the CES to identify depression.
The remaining participants were classified as healthy (N=95). These do not seem like balanced samples to me, at least (it would be nice if we knew what proportion of depressed vs not depressed agreed to share IG data).
Additionally, the appendices also mention that gender was not available for the depressed sample, which leads me to believe that they collected one sample of depressed participants and then another from the general population. This is a little shady (at least to me).
All that being said, I really like this paper. I think that the approach is novel, they use pretty good methods and it actually represents a contribution (if small) to human knowledge.
And I definitely went in with a prior against it.
The sample size is far too small to support their inferences, but that's a less hard problem to fix :)
70% of all depressed cases (n=37), with a relatively low number of
false alarms (n=23) and misses (n=17).
Edit: they mention an alternative model too that gives about 30% true positive rate for depression but is more accurate for non-depressed (probably since it mostly classifies people as non-depressed). The whole survey methodology and everything sounds suspect though, this study is just not something I'd put alot of weight on.
I've only had a quick scan through this but it seems like they were simply asking people if they were depressed. Is that correct? If so it seems very broad and unreliable. Why wouldn't they just use the standard PHQ-9 questionnaire to diagnose?
Standardized assessment has its issues (it doesn't replace a professional), but at least it works with a single definition that doesn't rely on calibrating each participant's understanding of depression. People struggling with depression often fail to recognize or be willing to admit it, and the opposite can be true as well.
By testing against participant answers, this study is actually determining if your photos correlate with saying you're depressed, which is a different and less predictable thing.
Also, I think detecting depression premtively via social media is a terrifying idea.
"We also checked metadata to assess whether an Instagram provided filter was applied to alter the appearance of a photograph."
"A closer look at filter usage in depressed versus healthy participants provided additional texture. Instagram filters were used differently by depressed and healthy individuals. In particular, depressed participants were less likely than healthy participants to use any filters at all. When depressed participants did employ filters, they most disproportionately favored the “Inkwell” filter, which converts color photographs to black and white images. Conversely, healthy participants most disproportionately favored the Valencia filter, which lightens the tint of photos."
> Also, I think detecting depression premtively via social media is a terrifying idea.
Yeah, they also called their model "Pre-diagnosis". :)
You support "I don't need this study" with prior studies.
So eventually, if you make a post with the name "Anonymous" on 4chan, the only record of your post will be on an archive website where they don't have your IP (so they can't semi-uniquely identify you) and your name is "Anonymous". That's virtually untraceable except with prior knowledge of posting habits (time of day and what board) and text analysis (which has shown to be quite effective in revealing authors behind pseudonyms).
When someone only has the content of your post, where you posted it, and at what time (the minimum amount of information 4chan lets you submit), we can express ourselves but quite effectively avoid employer snooping or otherwise.
Too many misspellings or rageful comments on social media would be a clear NO HIRE signal for me.
Why shouldn't they be?
I thought you are allowed to discriminate based on disability, if it impedes work function.
Any depression that gets to the level of being qualified as a disability seems severe enough to impede work.
Isn't that kind of totalitarian?
This has always existed, and you're more okay with it than you think you are: would you hire someone who says "nigger" in public?
The above is a prime example of the 'wrong kind' of public speech that makes you unemployable, as it should. Free speech doesn't mean "speech without consequence".
Yes. Obama said it in public, after all.
In the US the n-word is part of common speech, especially for young blacks. If you refused to hire anyone who said it in public, that would be a form of racist discrimination against black people.
This all relates to the racial double standard over permissible speech, of course.
> Free speech doesn't mean "speech without consequence".
In that case it isn't really free speech, any more than you've got free speech if I'm holding a gun to your head and threatening to shoot you if you say something I disagree with.
Further, if you don't understand that "free speech" refers to governmental policy rather than social norms, and if you don't understand that free speech comes with limitations (shouting "fire" in a crowded place, blah blah blah), then there is no hope of a thoughtful discussion.
Not all rights that you have on paper are enforced. A right to life is not the same as being alive. A right to free speech is not the same as actually having free speech. A society where saying unpopular things will get you attacked by a mob, instantly fired with loss of healthcare/housing, etc does not have free speech, even if the letter of the law says this right exists.
> shouting "fire" in a crowded place, blah blah blah
I've said this on the internet many, many times: That quote comes from a USSC case where the right to protest against the draft in the First World War was taken away, it was not upheld, and frankly, it's fascist.
If someone lived in the USSR, wouldn't it be right for them to protest against communism, even if they had to participate in the communist system to survive?
Would you tell them they should starve if they were against communism?
A society so averse to dissenting speech that it bears comparison with totalitarianism doesn't have to be state-enforced. The internet seems to do a good job of amplifying outrage and enabling witch hunts.
Is Arxiv considered a Peer Reviewed Journal or is it Cornell's submission database?
Their site really doesn't allude one way or the other so it makes me think its just a database.