Difference between revisions of "Mental health"
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[https://www.pnas.org/content/pnas/111/24/8788.full.pdf KGH][https://scholar.google.ch/scholar?hl=en&as_sdt=0%2C5&q=Experimental+evidence+of+massive-scale+emotional+contagion+through+social+networks&btnG= 14] tweaked the Facebook recommendation algorithm for a week, for three groups of subjects. One was the controlled group. For a second group, 10% of the posts with negative words were removed. For a third group, 10% of the posts with positive words were removed. The experiment showed that the second group then wrote more positive posts and fewer negative posts, while the third group wrote less positive posts and more negative posts. Effects are small. But the treatment and its length were small too. We discussed this paper in [https://www.youtube.com/watch?v=gQHvTow91FY RB3]. | [https://www.pnas.org/content/pnas/111/24/8788.full.pdf KGH][https://scholar.google.ch/scholar?hl=en&as_sdt=0%2C5&q=Experimental+evidence+of+massive-scale+emotional+contagion+through+social+networks&btnG= 14] tweaked the Facebook recommendation algorithm for a week, for three groups of subjects. One was the controlled group. For a second group, 10% of the posts with negative words were removed. For a third group, 10% of the posts with positive words were removed. The experiment showed that the second group then wrote more positive posts and fewer negative posts, while the third group wrote less positive posts and more negative posts. Effects are small. But the treatment and its length were small too. We discussed this paper in [https://www.youtube.com/watch?v=gQHvTow91FY RB3]. | ||
| − | On the other hand, [https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43887.pdf HOT][https://dblp.org/rec/bibtex/conf/kdd/HohnholdOT15 15] suggest that longer exposure could yield important effects. | + | On the other hand, [https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43887.pdf HOT][https://dblp.org/rec/bibtex/conf/kdd/HohnholdOT15 15] suggest that longer exposure could yield important effects. More precisely, they test learned ads "blindness" or "sightedness". Namely, they exposed some test group to greater ad loads, and tracked the click-through-rate of the test group, as opposed to, on each day, a randomly selected control group of users who got exposed to increased ad load on this single day. Crucially, they found that the learned ad blindness (or sightedness) was acquired after months. They modeled the learning by a function of the form <math>\alpha (1-e^{t/\tau})</math>, where <math>\tau \approx 83</math> days was the best fit. |
Latest revision as of 19:25, 6 February 2020
One of the most exciting AI opportunities is tackling large-scale mental health.
Loneliness
Kurzgesagt19 stresses the fact that loneliness is a growing problem, which leads to other undesirable consequences like increased cancer risks, ageing and aggressivity.
Impact
KGH14 tweaked the Facebook recommendation algorithm for a week, for three groups of subjects. One was the controlled group. For a second group, 10% of the posts with negative words were removed. For a third group, 10% of the posts with positive words were removed. The experiment showed that the second group then wrote more positive posts and fewer negative posts, while the third group wrote less positive posts and more negative posts. Effects are small. But the treatment and its length were small too. We discussed this paper in RB3.
On the other hand, HOT15 suggest that longer exposure could yield important effects. More precisely, they test learned ads "blindness" or "sightedness". Namely, they exposed some test group to greater ad loads, and tracked the click-through-rate of the test group, as opposed to, on each day, a randomly selected control group of users who got exposed to increased ad load on this single day. Crucially, they found that the learned ad blindness (or sightedness) was acquired after months. They modeled the learning by a function of the form [math]\alpha (1-e^{t/\tau})[/math], where [math]\tau \approx 83[/math] days was the best fit.
