Understanding Effects of Moderation and Migration on Online Video Sharing Platforms

Gabriel Luis Santos Freire, Tales Panoutsos, Lucas Perez, Fabrício Benevenuto, Flavio V. D. de Figueiredo

Abstract: To mitigate the propagation of potentially dangerous information (e.g., fake news), social media platforms usually rely on the deletion or censoring of content (here called moderation). In this research, we measure how content moderation on YouTube affects a channel’s popularity. To achieve our goal, we gather information on videos that were deleted from YouTube using the altCensored platform. We cross-section this data with channel popularity time series from SocialBlade. After characterizing this novel dataset, we employ Regression Discontinuity Design (RDD) to effectively measure impact. Using RDD we categorize the impact of censorship on deletion in four different patterns: (PP) channels with positive regression slopes (e.g., indicating growth) both before and after deletion; (PN) channels with positive growth before deletion and negative after, capturing a positive-negative relation, or a deletion that inverts the trend, (NP) negative-positive, those which were decreasing in growth with a change in trend after deletion (NN) as well as negative to negative. These groups represent 16% (PP), 26% (PN), 16% (NP), and 42% (NN) of our moderated videos. As a final result, we also show that videos may yet be found on other websites. The large amounts of events in (PP) and (NP), as well as the fact that videos are still available on the Web, indicate that moderation may not be as effective as it seems.