Automatically measuring the quality of user generated content in forums

Authors:
Kevin Chai;Chen Wu;Vidyasagar Potdar;Pedram Hayati
Affiliations:
Digital Ecosystems and Business Institute, Curtin University International Centre for Radio Astronomy Research, University of Western Australia, Australia;Digital Ecosystems and Business Institute, Curtin University International Centre for Radio Astronomy Research, University of Western Australia, Australia;Digital Ecosystems and Business Institute, Curtin University International Centre for Radio Astronomy Research, University of Western Australia, Australia;Digital Ecosystems and Business Institute, Curtin University International Centre for Radio Astronomy Research, University of Western Australia, Australia
Venue:
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Year:
2011

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Abstract

The amount of user generated content on the Web is growing and identifying high quality content in a timely manner has become a problem. Many forums rely on its users to manually rate content quality but this often results in gathering insufficient rating. Automated quality assessment models have largely evaluated linguistic features but these techniques are less adaptive for the diverse writing styles and terminologies used by different forum communities. Therefore, we propose a novel model that evaluates content, usage, reputation, temporal and structural features of user generated content to address these limitations. We employed a rule learner, a fuzzy classifier and Support Vector Machines to validate our model on three operational forums. Our model outperformed the existing models in our experiments and we verified that our performance improvements were statistically significant.