Name
Abstract | ||
|---|---|---|
The web has become a large knowledge provider for society, allowing people to not just consume information but also produce it. Collaborative documents bring some significant advantages and decentralization, but they also raise questions concerning its quality. In this work, we explore the quality assessment on collaborative documents using these documents' topics. The proposed approach improved in 3.2% the accuracy of quality assesment of Wikipedia content. Then, the main contribution in this paper is an analysis of how we can use topic modelling in order to improve quality prediction performance. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3323503.3360628 | WEBMEDIA 2019: PROCEEDINGS OF THE 25TH BRAZILLIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB |
Keywords | Field | DocType |
Latent Dirichlet Allocation, Information Quality, Topic Prediction, Automatic Quality Assessment, Machine Learning | Information retrieval,Computer science,Topic model,Multimedia | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lauro C. J. Santos | 1 | 0 | 0.34 |
| Taís Christofani | 2 | 0 | 0.34 |
| Ismael S. Silva | 3 | 0 | 1.01 |
| Daniel Hasan Dalip | 4 | 140 | 11.56 |