Deep learning model for classifying drug abuse risk behavior in tweets

Han Hu; Pranavi Moturu; Kannan Neten Dharan; James Geller; Sophie Di Iorio; Hai Phan

doi:10.1109/ICHI.2018.00066

Deep learning model for classifying drug abuse risk behavior in tweets

Han Hu, Pranavi Moturu, Kannan Neten Dharan, James Geller, Sophie Di Iorio, Hai Phan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

16 Scopus citations

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.

Original language	English (US)
Title of host publication	Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	386-387
Number of pages	2
ISBN (Electronic)	9781538653777
DOIs	https://doi.org/10.1109/ICHI.2018.00066
State	Published - Jul 24 2018
Event	6th IEEE International Conference on Healthcare Informatics, ICHI 2018 - New York, United States Duration: Jun 4 2018 → Jun 7 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018

Other

Other	6th IEEE International Conference on Healthcare Informatics, ICHI 2018
Country/Territory	United States
City	New York
Period	6/4/18 → 6/7/18

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Networks and Communications
Health Informatics

Keywords

Deep Learning
Drug Abuse Detection
Social Media
Twitter

Access to Document

10.1109/ICHI.2018.00066

Cite this

Hu, H., Moturu, P., Dharan, K. N., Geller, J., Di Iorio, S., & Phan, H. (2018). Deep learning model for classifying drug abuse risk behavior in tweets. In Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018 (pp. 386-387). Article 8419399 (Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICHI.2018.00066

Hu, Han ; Moturu, Pranavi ; Dharan, Kannan Neten et al. / Deep learning model for classifying drug abuse risk behavior in tweets. Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 386-387 (Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018).

@inproceedings{c9f8df858d2f46a0a45ce931c62557ae,

title = "Deep learning model for classifying drug abuse risk behavior in tweets",

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.",

keywords = "Deep Learning, Drug Abuse Detection, Social Media, Twitter",

author = "Han Hu and Pranavi Moturu and Dharan, {Kannan Neten} and James Geller and {Di Iorio}, Sophie and Hai Phan",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 6th IEEE International Conference on Healthcare Informatics, ICHI 2018 ; Conference date: 04-06-2018 Through 07-06-2018",

year = "2018",

month = jul,

day = "24",

doi = "10.1109/ICHI.2018.00066",

language = "English (US)",

series = "Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018",

address = "United States",

}

Hu, H, Moturu, P, Dharan, KN, Geller, J, Di Iorio, S & Phan, H 2018, Deep learning model for classifying drug abuse risk behavior in tweets. in Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018., 8419399, Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018, Institute of Electrical and Electronics Engineers Inc., pp. 386-387, 6th IEEE International Conference on Healthcare Informatics, ICHI 2018, New York, United States, 6/4/18. https://doi.org/10.1109/ICHI.2018.00066

Deep learning model for classifying drug abuse risk behavior in tweets. / Hu, Han; Moturu, Pranavi; Dharan, Kannan Neten et al.
Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 386-387 8419399 (Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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AU - Hu, Han

AU - Moturu, Pranavi

AU - Dharan, Kannan Neten

AU - Geller, James

AU - Di Iorio, Sophie

AU - Phan, Hai

PY - 2018/7/24

Y1 - 2018/7/24

N2 - 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.

AB - 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.

KW - Deep Learning

KW - Drug Abuse Detection

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M3 - Conference contribution

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BT - Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018

PB - Institute of Electrical and Electronics Engineers Inc.

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Hu H, Moturu P, Dharan KN, Geller J, Di Iorio S, Phan H. Deep learning model for classifying drug abuse risk behavior in tweets. In Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 386-387. 8419399. (Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018). doi: 10.1109/ICHI.2018.00066

Deep learning model for classifying drug abuse risk behavior in tweets

Abstract

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