Name
Abstract | ||
|---|---|---|
Illicit drug use is a serious problem around the world. Social media has increasingly become an important tool for analyzing drug use patterns and monitoring emerging drug abuse trends. Accurately retrieving illicit drug-related social media posts is an important step in this research. Frequently, hash tags are used to identify and retrieve posts on a specific topic. However hash tags are highly ambiguous. Posts with the same hash tags are not always on the same topic. Moreover, hash tags are evolving, especially those related to illicit drugs. New street names are introduced constantly to avoid detection. In this paper, we employ topic modeling to disambiguate hash tags and track the changes of hashtags using semantic word embedding. Our preliminary evaluation shows the promise of these methods. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) | Hashtags,Post,Topic |
Field | DocType | ISSN |
World Wide Web,Social media,Computer science,Word embedding,Topic model,Probabilistic logic,Artificial neural network | Conference | 2156-1125 |
Citations | PageRank | References |
0 | 0.34 | 24 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Ding | 1 | 15 | 8.48 |
| arpita roy | 2 | 14 | 4.39 |
| Zhiyuan Chen | 3 | 90 | 8.15 |
| Qian Zhu | 4 | 76 | 8.54 |
| Shimei Pan | 5 | 684 | 64.41 |