Predicting YouTube video viewcount with Twitter feed
Honglin Yu
CSIRO ICT IR and friendsDATE: 2013-10-28
TIME: 16:00:00 - 17:00:00
LOCATION: CSIRO seminar room
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ABSTRACT:
Our recent work proposes a novel method to use Twitter features to predict two difficult cases of content popularity on YouTube a" the sudden jump in viewcount, and the viewcount of newly uploaded videos. User influence in Twitter and content popularity on YouTube are both very active areas of research, but little attention was devoted to measuring the effects of the former on the latter. We define two classification problems for viewcount jump and new video popularity, respectively. We extracted four types of features from Twitter, including information about tweets, Twitter user graph, and the interactions that users perform and receive. Prediction performances are reported on thousands of YouTube videos mentioned in a 3-month Twitter feed from 2009. The accuracy for predicting jump improves by 0.10 over a baseline of viewcount history; the accuracy for predicting early popularity improves by 0.25 over random baseline, where no history is available. These promising results will help a range of applications, including content recommendation on social media, advertising, and others.
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