Name
Abstract | ||
|---|---|---|
Recently, crowdsourcing systems have been adopted for promoting products in online social networks (OSN), e.g., Twitter. We call it the crowdsourced promotion. When promoting products using crowdsourcing systems, it is critical to qualify the effectiveness of such promotions in OSN. One possible solution is to use conventional attributes for the characteristics of workers such as worker levels, the number of followers, and Klout scores. Unlike existing crowdsourcing tasks that are performed in crowdsourcing systems, crowdsourced promotions are mainly performed in OSN. Therefore, conventional attributes for workers are ineffective for validating the quality of crowdsourced promotions. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.ins.2017.12.004 | Information Sciences |
Field | DocType | Volume |
Data science,Social network,Crowdsourcing,Artificial intelligence,Classifier (linguistics),Empirical research,Mathematics,Machine learning | Journal | 432 |
ISSN | Citations | PageRank |
0020-0255 | 1 | 0.36 |
References | Authors | |
17 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hee-Jeong Kim | 1 | 1 | 0.36 |
| Jongwuk Lee | 2 | 7 | 5.61 |
| Dong-Kyu Chae | 3 | 59 | 10.07 |
| Sang-Wook Kim | 4 | 792 | 152.77 |