Video and textual tutorial, on the basic usage, the evidence page, and everything a researcher need to start analyzing YT!
A collective observation of the Youtube personalization algorithm; addressing COVID-19 and getting evidence on how recommendations works
Coordinated collection of yt search results; to spot tendencies, bubbles, and biases. real time psedonymized open data feed.
During the US elections, we realized a collective project at the Digital Methods Winter School 2021. We simulated echo chambers, we studied the construction of filter bubbles and consequent political polarization of suggestions.
During Brexit, we made a three days analysis of the algorithm with ten researchers from all over the world. The research aims to split the group in two and see how YT considers different activities to personalize the following recommendation.
Our first research with a dozen of students: we began by mapping Youtube personalization differences and distances. Watching a video for a few seconds more is enough to get different recommendations. We tested the sperimental 'clean browser'.