Name
Abstract | ||
|---|---|---|
Social media such as Twitter can provide urgently needed drug abuse intelligence to support the campaign of fighting against the national drug abuse crisis. We employed a targeted tweet collection approach and a two-staged annotation strategy that combines conventional annotation with crowdsourced annotation to produce annotated training dataset. In this demo, we share deep learning models trained in a boosting manner using the data from the two-staged annotation method and unlabeled data collection to detect drug abuse risk behavior in tweets. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICHI.2018.00066 | 2018 IEEE International Conference on Healthcare Informatics (ICHI) |
Keywords | Field | DocType |
drug abuse detection, social media, deep learning, Twitter | Data science,Data modeling,Data collection,Social media,Annotation,Computer science,Substance abuse,Boosting (machine learning),Artificial intelligence,Public healthcare,Deep learning | Conference |
ISBN | Citations | PageRank |
978-1-5386-5378-4 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Han Hu | 1 | 193 | 14.98 |
| Pranavi Moturu | 2 | 0 | 0.34 |
| Kannan Dharan | 3 | 0 | 0.34 |
| James Geller | 4 | 33 | 5.08 |
| Sophie Iorio | 5 | 0 | 0.34 |
| Hai Phan | 6 | 0 | 0.34 |
| Huy T. Vo | 7 | 1035 | 61.10 |
| Soon Ae Chun | 8 | 893 | 100.67 |