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Anticipating discussion activity on community forums

Rowe, Matthew; Angeletou, Sofia and Alani, Harith (2011). Anticipating discussion activity on community forums. In: Third IEEE International Conference on Social Computing (SocialCom2011), 9-11 October 2011, Boston, MA, USA, pp. 315–322.

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URL: http://www.iisocialcom.org/conference/socialcom201...
DOI (Digital Object Identifier) Link: http://dx.doi.org/10.1109/PASSAT/SocialCom.2011.215
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Abstract

Attention economics is a vital component of the Social Web, where the sheer magnitude and rate at which social data is published forces web users to decide on what content to focus their attention on. By predicting popular posts on the Social Web, that contain lengthy discussions and debates, analysts can focus their attention more effectively on content that is deemed more influential. In this paper we present a two-step approach to anticipate discussions in community forums by a) identifying seed posts - i.e., posts that generate discussions, and b) predicting the length of these discussions. We explore the effectiveness of a range of features in anticipating discussions such as user and content features, and present focus features that capture the topical concentration of a user. For identifying seed posts we show that content features are better predictors than user features, while achieving an F1 value of 0.792 when using all features. For predicting discussion activity we find a positive correlation between the focus of the user and discussion volumes, and achieve an nDCG@1 value of 0.89 when predicting using user features.

Item Type: Conference Item
Copyright Holders: Not known
Project Funding Details:
Funded Project NameProject IDFunding Body
Not Set248512EU-FP7 projects WeGov
Not Set257859Robust
Academic Unit/Department: Knowledge Media Institute
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 29580
Depositing User: Kay Dave
Date Deposited: 26 Sep 2011 09:58
Last Modified: 22 Nov 2013 18:09
URI: http://oro.open.ac.uk/id/eprint/29580
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