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Forecasting audience increase on YouTube

Rowe, Matthew (2011). Forecasting audience increase on YouTube. In: Workshop on User Profile Data on the Social Semantic Web, 8th Extended Semantic Web Conference 2011 (ESWC 2011), 30 May 2011, Heraklion, 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 a!ect 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: Conference Item
Copyright Holders: 2011 The Author
Project Funding Details:
Funded Project NameProject IDFunding Body
Not SetNot SetEC-FP7 project Robust [257859]
Keywords: user modelling; forecasting; social web; data mining; behaviour
Academic Unit/Department: Knowledge Media Institute
Item ID: 28845
Depositing User: Kay Dave
Date Deposited: 31 May 2011 13:05
Last Modified: 23 Oct 2012 21:38
URI: http://oro.open.ac.uk/id/eprint/28845
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