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Anticipating discussion activity on community forums

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Anticipating discussion activity on community forums. / Rowe, Matthew; Angeletou, Sofia; Alani, Harith.
Privacy, security, risk and trust (passat), 2011 ieee third international conference on and 2011 ieee third international conference on social computing (socialcom). IEEE, 2011. p. 315-322.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Rowe, M, Angeletou, S & Alani, H 2011, Anticipating discussion activity on community forums. in Privacy, security, risk and trust (passat), 2011 ieee third international conference on and 2011 ieee third international conference on social computing (socialcom). IEEE, pp. 315-322, IEEE Third International Conference on Social Computing (socialcom) 2011, Boston, United States, 18/09/11. https://doi.org/10.1109/PASSAT/SocialCom.2011.215

APA

Rowe, M., Angeletou, S., & Alani, H. (2011). Anticipating discussion activity on community forums. In Privacy, security, risk and trust (passat), 2011 ieee third international conference on and 2011 ieee third international conference on social computing (socialcom) (pp. 315-322). IEEE. https://doi.org/10.1109/PASSAT/SocialCom.2011.215

Vancouver

Rowe M, Angeletou S, Alani H. Anticipating discussion activity on community forums. In Privacy, security, risk and trust (passat), 2011 ieee third international conference on and 2011 ieee third international conference on social computing (socialcom). IEEE. 2011. p. 315-322 doi: 10.1109/PASSAT/SocialCom.2011.215

Author

Rowe, Matthew ; Angeletou, Sofia ; Alani, Harith. / Anticipating discussion activity on community forums. Privacy, security, risk and trust (passat), 2011 ieee third international conference on and 2011 ieee third international conference on social computing (socialcom). IEEE, 2011. pp. 315-322

Bibtex

@inproceedings{e0b096a4a88d406290d5c5c8f314d0cc,
title = "Anticipating discussion activity on community forums",
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.",
keywords = "Communities , Discussions, Prediction , Social Web",
author = "Matthew Rowe and Sofia Angeletou and Harith Alani",
year = "2011",
month = oct,
day = "1",
doi = "10.1109/PASSAT/SocialCom.2011.215",
language = "English",
isbn = "978-1-4577-1931-8",
pages = "315--322",
booktitle = "Privacy, security, risk and trust (passat), 2011 ieee third international conference on and 2011 ieee third international conference on social computing (socialcom)",
publisher = "IEEE",
note = "IEEE Third International Conference on Social Computing (socialcom) 2011 ; Conference date: 18-09-2011",

}

RIS

TY - GEN

T1 - Anticipating discussion activity on community forums

AU - Rowe, Matthew

AU - Angeletou, Sofia

AU - Alani, Harith

PY - 2011/10/1

Y1 - 2011/10/1

N2 - 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.

AB - 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.

KW - Communities

KW - Discussions

KW - Prediction

KW - Social Web

U2 - 10.1109/PASSAT/SocialCom.2011.215

DO - 10.1109/PASSAT/SocialCom.2011.215

M3 - Conference contribution/Paper

SN - 978-1-4577-1931-8

SP - 315

EP - 322

BT - Privacy, security, risk and trust (passat), 2011 ieee third international conference on and 2011 ieee third international conference on social computing (socialcom)

PB - IEEE

T2 - IEEE Third International Conference on Social Computing (socialcom) 2011

Y2 - 18 September 2011

ER -