Hey, training is the machine's job! First, we need our data set. We want: credit scores (possibly the Equifax breach?), a corpus of political views (scrape Twitter), and a way of linking the two (e.g. filter for verified profiles, extract name and look for town references.)
Once we have that, identify several sets: the "nobles" are hand-picked by us as shining examples of $COUNTRY values, and assigned value 1, "undesirables" are ranked in steps from [-1,0], and the "serfs" are the ones we're monitoring. Labeling nobles and undesirables the most time-consuming part of this exercise, it's recommended to outsource to some political instructors.
Once you have that, mix the sets together and create a deep neural net with a compact feature encoding layer at the top, say 128 neurons, which maps to a scalar value in range [-1,1] to determine social credit. Argmax for a few thousand iters.
Once you're done, you'll have a feature encoding network you can deploy on your country's firewall and search engines, while you can hide the credit calculator in a Party-controlled central server.
Enjoy!
EDIT: D'oh, totally forgot about clique analysis. Honestly that can be handled without ML, you can just assign credit' = min(credit, friend_credit) where friends are one edge away. Run that iteratively with an exponential discount factor in the delta change from credit->credit' and you'll converge on a good answer.
Once we have that, identify several sets: the "nobles" are hand-picked by us as shining examples of $COUNTRY values, and assigned value 1, "undesirables" are ranked in steps from [-1,0], and the "serfs" are the ones we're monitoring. Labeling nobles and undesirables the most time-consuming part of this exercise, it's recommended to outsource to some political instructors.
Once you have that, mix the sets together and create a deep neural net with a compact feature encoding layer at the top, say 128 neurons, which maps to a scalar value in range [-1,1] to determine social credit. Argmax for a few thousand iters.
Once you're done, you'll have a feature encoding network you can deploy on your country's firewall and search engines, while you can hide the credit calculator in a Party-controlled central server.
Enjoy!
EDIT: D'oh, totally forgot about clique analysis. Honestly that can be handled without ML, you can just assign credit' = min(credit, friend_credit) where friends are one edge away. Run that iteratively with an exponential discount factor in the delta change from credit->credit' and you'll converge on a good answer.