The Open UniversitySkip to content

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 Oct 2011, Boston, MA, USA, pp. 315–322.

Full text available as:
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (819kB)
DOI (Digital Object Identifier) Link:
Google Scholar: Look up in Google Scholar


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 or Workshop Item
Copyright Holders: Not known
Project Funding Details:
Funded Project NameProject IDFunding Body
Not Set248512EU-FP7 projects WeGov
Not Set257859Robust
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 29580
Depositing User: Kay Dave
Date Deposited: 26 Sep 2011 09:58
Last Modified: 07 Dec 2018 18:58
Share this page:


Altmetrics from Altmetric

Citations from Dimensions

Download history for this item

These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.

Actions (login may be required)

Policies | Disclaimer

© The Open University   contact the OU