████: "hey all, the Instagram teen's team is currently doing some data analysis around local for teens. Specifically we are interested to study teen penetration and engagement on school level in US. So our question on our end is, let's say we have location collection enabled (via geoAPI) and we know someone is a teen. Is it possible for us to infer which high school they go to from geoAPI, purely for analysis purposes, so it's fine if the data is anonymous"
████: "you can mapMAPMonthly Active PeopleCore Meta engagement metric counting distinct people active on a platform in a month. Paired with DAP in internal goal-setting; the MAP/DAP ratio is used as a measure of how frequently a monthly user returns.Full entry ↗ the location to a polygon if there as an entity for the school with a polygon associated to it"
████: "this is just tech feasability commentary i dont know what the privacy implications here would be on person-to-place"
████: "Just clarification: so there is capability for us to match a location to school then so long as that school is an entity matched to the polygon?"
████: "you also have to run the analysis during school hours haha"
████: "otherwise then location will update out of school"
████: "Wait you mean the data is real time and not stored for offline purposes?"
████: "yup"
████: "Do we not store the location of teens throughout the day across multiple days?"
████: "nope, we get one location and then we delete it"
████: "when we get a new one"
████: "Oh 😕 so basically if they open the app somewhere that is not the school most recently, then this changes"
████: "I.e. When they're at home or on weekends"
████: "yeah..."
████: "Is the reasoning for this because of privacy concerns?"
████: "yup, + storage costs + battery drain"
████: "Also they have to actually open the app AT school for us to hope to get the school location"
████: "Do we hope to eventually be at a place where we might machine learn a teens 'important' locations?"
Meta & Children's Online Safety