Name
Abstract | ||
|---|---|---|
Web search is widely used to find online medical advice. As such, health-related information access requires retrieval algorithms capable of promoting reliable documents and filtering out unreliable ones. To this end, different types of components, such as query-document matching features, passage relevance estimation and AI-based reliability estimators, need to be combined. In this paper, we propose an entire pipeline for misinformation detection, based on the fusion of multiple content-based features. We present experiments which study the influence of each pipeline stage for the target task. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.engappai.2022.105211 | Engineering Applications of Artificial Intelligence |
Keywords | DocType | Volume |
Engineering applications,Web search,Health misinformation,Information retrieval,Natural language processing,Artificial intelligence,Deep learning for natural language processing | Journal | 115 |
ISSN | Citations | PageRank |
0952-1976 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcos Fernandez-Pichel | 1 | 0 | 0.34 |
| David E. Losada | 2 | 326 | 40.63 |
| Juan C. Pichel | 3 | 0 | 0.34 |