Abstract
JUUL has become a widely used brand of e-cigarettes which takes more than 70% of the market. Social media provides a popular platform for users to discuss the preference and perceptions of JUUL. The discussions are valuable for real-time monitoring of JUUL use. Current research on topic analysis of JUUL discussions mainly relies on human work, which takes much time and effort. This paper adopts a Semantics-assisted NMF topic analysis model to automatically discover topics from JUUL-related short posts on Reddit. By successfully merging the semantic relationships into traditional NMF, this model outperforms in discovering topics with keywords that are important but have a lower word frequency among the posts. Experimental results show the potential of this model in JUUL surveillance and control practice.
| Original language | English (US) |
|---|---|
| Title of host publication | 2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019 |
| Editors | Xiaolong Zheng, Ahmed Abbasi, Michael Chau, Alan Wang, Lina Zhou |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 212-214 |
| Number of pages | 3 |
| ISBN (Electronic) | 9781728125046 |
| DOIs | |
| State | Published - Jul 2019 |
| Externally published | Yes |
| Event | 17th IEEE International Conference on Intelligence and Security Informatics, ISI 2019 - Shenzhen, China Duration: Jul 1 2019 → Jul 3 2019 |
Publication series
| Name | 2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019 |
|---|
Conference
| Conference | 17th IEEE International Conference on Intelligence and Security Informatics, ISI 2019 |
|---|---|
| Country | China |
| City | Shenzhen |
| Period | 7/1/19 → 7/3/19 |
Fingerprint
Keywords
- E-cigarettes
- JUUL
- Short posts
- Topic analysis
ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality
- Computer Vision and Pattern Recognition
- Information Systems and Management
- Information Systems
Cite this
Analyzing topics of JUUL discussions on social media using a semantics-assisted NMF model. / Liu, Hejing; Li, Qiudan; Yao, Riheng; Zeng, Daniel Dajun.
2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019. ed. / Xiaolong Zheng; Ahmed Abbasi; Michael Chau; Alan Wang; Lina Zhou. Institute of Electrical and Electronics Engineers Inc., 2019. p. 212-214 8823541 (2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Analyzing topics of JUUL discussions on social media using a semantics-assisted NMF model
AU - Liu, Hejing
AU - Li, Qiudan
AU - Yao, Riheng
AU - Zeng, Daniel Dajun
PY - 2019/7
Y1 - 2019/7
N2 - JUUL has become a widely used brand of e-cigarettes which takes more than 70% of the market. Social media provides a popular platform for users to discuss the preference and perceptions of JUUL. The discussions are valuable for real-time monitoring of JUUL use. Current research on topic analysis of JUUL discussions mainly relies on human work, which takes much time and effort. This paper adopts a Semantics-assisted NMF topic analysis model to automatically discover topics from JUUL-related short posts on Reddit. By successfully merging the semantic relationships into traditional NMF, this model outperforms in discovering topics with keywords that are important but have a lower word frequency among the posts. Experimental results show the potential of this model in JUUL surveillance and control practice.
AB - JUUL has become a widely used brand of e-cigarettes which takes more than 70% of the market. Social media provides a popular platform for users to discuss the preference and perceptions of JUUL. The discussions are valuable for real-time monitoring of JUUL use. Current research on topic analysis of JUUL discussions mainly relies on human work, which takes much time and effort. This paper adopts a Semantics-assisted NMF topic analysis model to automatically discover topics from JUUL-related short posts on Reddit. By successfully merging the semantic relationships into traditional NMF, this model outperforms in discovering topics with keywords that are important but have a lower word frequency among the posts. Experimental results show the potential of this model in JUUL surveillance and control practice.
KW - E-cigarettes
KW - JUUL
KW - Short posts
KW - Topic analysis
UR - http://www.scopus.com/inward/record.url?scp=85072964964&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072964964&partnerID=8YFLogxK
U2 - 10.1109/ISI.2019.8823541
DO - 10.1109/ISI.2019.8823541
M3 - Conference contribution
AN - SCOPUS:85072964964
T3 - 2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019
SP - 212
EP - 214
BT - 2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019
A2 - Zheng, Xiaolong
A2 - Abbasi, Ahmed
A2 - Chau, Michael
A2 - Wang, Alan
A2 - Zhou, Lina
PB - Institute of Electrical and Electronics Engineers Inc.
ER -