Thank you for this post, Mark! I appreciate that you included the graph, though I’m not sure how to interpret it. Do you mind explaining what the “recommendation impression advantage” is? (I’m sure you explain this in great detail in your paper, so feel free to ignore me or say “go read the paper” :D).
The main question that pops out for me is “advantage relative to what?” I imagine a lot of people would say “even if YouTube’s algorithm is less likely to recommend [conspiracy videos/propaganda/fake news] than [traditional media/videos about cats], then it’s still a problem! Any amount of recommending [bad stuff that is harmful/dangerous/inaccurate] should not be tolerated!”
Yeas, I agree with that. Definitely a lot of room for criticism and different points of view about what should be removed, or sans-recommended. My main effort here is to make sure people know what is happening.
Thank you for this post, Mark! I appreciate that you included the graph, though I’m not sure how to interpret it. Do you mind explaining what the “recommendation impression advantage” is? (I’m sure you explain this in great detail in your paper, so feel free to ignore me or say “go read the paper” :D).
The main question that pops out for me is “advantage relative to what?” I imagine a lot of people would say “even if YouTube’s algorithm is less likely to recommend [conspiracy videos/propaganda/fake news] than [traditional media/videos about cats], then it’s still a problem! Any amount of recommending [bad stuff that is harmful/dangerous/inaccurate] should not be tolerated!”
What would you say to those people?
Recommendation advantage is the ratio of impressions sent vs received. https://github.com/markledwich2/recfluence#calculations-and-considerations
Yeas, I agree with that. Definitely a lot of room for criticism and different points of view about what should be removed, or sans-recommended. My main effort here is to make sure people know what is happening.