Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-25832-9_6 | Australasian Conference on Artificial Intelligence |
Keywords | Field | DocType |
support vector machines,automated quality assessment model,different forum community,fuzzy classifier,novel model,diverse writing style,existing model,insufficient rating,rate content quality,high quality content,user generated content | User-generated content,World Wide Web,Information retrieval,Computer science,Writing style,Support vector machine,Fuzzy classifier,Reputation | Conference |
Volume | ISSN | Citations |
7106 | 0302-9743 | 4 |
PageRank | References | Authors |
0.50 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kevin Chai | 1 | 67 | 4.94 |
| Chen Wu | 2 | 303 | 14.13 |
| Vidyasagar Potdar | 3 | 303 | 35.24 |
| Pedram Hayati | 4 | 52 | 5.64 |