@inproceedings{e9e7e0659b1b4676859d870f58ff1191,
title = "Analyzing topics of JUUL discussions on social media using a semantics-assisted NMF model",
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.",
keywords = "E-cigarettes, JUUL, Short posts, Topic analysis",
author = "Hejing Liu and Qiudan Li and Riheng Yao and Zeng, {Daniel Dajun}",
year = "2019",
month = jul,
doi = "10.1109/ISI.2019.8823541",
language = "English (US)",
series = "2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "212--214",
editor = "Xiaolong Zheng and Ahmed Abbasi and Michael Chau and Alan Wang and Lina Zhou",
booktitle = "2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019",
note = "17th IEEE International Conference on Intelligence and Security Informatics, ISI 2019 ; Conference date: 01-07-2019 Through 03-07-2019",
}