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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:||
|Keywords:||user modelling; forecasting; social web; data mining; behaviour|
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Depositing User:||Kay Dave|
|Date Deposited:||31 May 2011 13:05|
|Last Modified:||05 Oct 2016 17:57|
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