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are shown to be due to differences in how content is consumed on the two portals: Digg stories quickly become outdated, while Youtube videos are still found long after they are initially submitted to the portal.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 http://www.hpl.hp.com/research/scl/papers/sncf/ User-generated social networking links can be as predictive as algorithmically identified "neighbors" in recommender systems. collaborative filtering social networks prediction recommender systems essembly This paper demonstrates that "social network collaborative filtering" (SNCF), wherein user-selected like-minded alters are used to make predictions, can rival traditional user-to-user collaborative filtering (CF) in predictive accuracy. Using a unique data set from an online community where users rated items and also created social networking links specifically intended to represent like-minded allies, we use SNCF and traditional CF to predict ratings by networked users. We find that SNCF using generic "friend" alters is moderately worse than the better CF techniques, but outperforms benchmarks such as by-item or by-user average rating; generic friends often are not like-minded. However, SNCF using "ally" alters is competitive with CF. These results are significant because SNCF is tremendously more computationally efficient than traditional user-user CF and may be implemented in large-scale web commerce and social networking communities. It is notoriously difficult to distinguish the contributions of social influence (where allies influence users) and social selection (where users are simply effective at selecting like-minded people as their allies). Nonetheless, comparing similarity over time, we do show no evidence of strong social influence among allies or friends. Rong Zheng, Dennis M. Wilkinson and Foster Provost 2008-10-06 12:00:00