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Forecasting audience increase on Youtube

Rowe, Matthew (2011) Forecasting audience increase on Youtube. In: Proceedings of the International Workshop on User Profile Data on the Social Semantic Web co-located with 8th Extended Semantic Web Conference May 30, 2011, Heraklion, Crete, Greece. .

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Abstract

User profiles constructed on Social Web platforms are often motivated by the need to maximise user reputation within a community. Subscriber, or follower, counts are an indicator of the influence and standing that the user has, where greater values indicate a greater perception or regard for what the user has to say or share. However, at present there lacks an understanding of the factors that lead to an increase in such audience levels, and how a user’s behaviour can affect their reputation. In this paper we attempt to fill this gap, by examining data collected from YouTube over regular time intervals. We explore the correlation between the subscriber counts and several behaviour features - extracted from both the user’s profile and the content they have shared. Through the use of a Multiple Linear Regression model we are able to forecast the audience levels that users will yield based on observed behaviour. Combining such a model with an exhaustive feature selection process, we yield statistically significant performance over a baseline model containing all features.

Item Type: Contribution in Book/Report/Proceedings
Uncontrolled Keywords: User modelling ; Forecasting ; Social Web ; Data Mining ; Behaviour
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 58629
Deposited By: ep_importer_pure
Deposited On: 26 Sep 2012 11:50
Refereed?: No
Published?: Published
Last Modified: 15 Aug 2013 12:14
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/58629

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