http://www.hpl.hp.com/research/scl/papers/persistence/persistence.pdf How persistence does not lead to success A hallmark of the attention economy is the competition for the attention of others in information rich environments. Thus people persistently upload content to social media sites, hoping for the highly unlikely outcome of topping the charts and reaching a wide audience. And yet, an analysis of the production histories and success dynamics of 10 million videos from Youtube revealed that the more frequently an individual uploads content the less likely it is that it will reach a success threshold. This paradoxical result is further compounded by the fact that the average quality of submissions does increase with the number of uploads, and also that the likelihood success is less than that of playing a lottery. Fang Wu and Bernardo Huberman 2009-03-21 00:33:00 attention persistence success social computing lotteries youtube http://www.hpl.hp.com/research/scl/papers/predictions popularity, youtube, digg, attention, predicting future downloads. attention youtube popularity social media prediction online content We present a method for accurately predicting the long time popularity of online content from early measurements of user access. Using two content sharing portals, Youtube and Digg, we show that by modeling the accrual of views and votes on content offered by these services we can predict the long-term dynamics of individual submissions from initial data. In the case of Digg, measuring access to given stories during the first two hours allows us to forecast their popularity 30 days ahead with remarkable accuracy, while downloads of Youtube videos need to be followed for 10 days to attain the same performance. The differing time scales of the predictions are shown to be due to differences in how content is consumed on the two portals: Digg stories quickly become outdated, while Youtube videos are still found long after they are initially submitted to the portal. We show that predictions are more accurate for submissions for which attention decays quickly, whereas predictions for evergreen content will be prone to larger errors. Gabor Szabo and Bernardo A. Huberman 2008-11-03 15:27:00