Deep self-taught learning for detecting drug abuse risk behavior in tweets

Han Hu; Nhat Hai Phan; James Geller; Huy Vo; Bhole Manasi; Xueqi Huang; Sophie Di Lorio; Thang Dinh; Soon Ae Chun

doi:10.1007/978-3-030-04648-4_28

Deep self-taught learning for detecting drug abuse risk behavior in tweets

Han Hu, Nhat Hai Phan, James Geller, Huy Vo, Bhole Manasi, Xueqi Huang, Sophie Di Lorio, Thang Dinh, Soon Ae Chun

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

14 Scopus citations

Abstract

Drug abuse continues to accelerate toward becoming the most severe public health problem in the United States. The ability to detect drug abuse risk behavior at a population scale, such as among the population of Twitter users, can help us to monitor the trend of drug-abuse incidents. Unfortunately, traditional methods do not effectively detect drug abuse risk behavior, given tweets. This is because: (1) Tweets usually are noisy and sparse; and (2) The availability of labeled data is limited. To address these challenging problems, we proposed a deep self-taught learning system to detect and monitor drug abuse risk behaviors in the Twitter sphere, by leveraging a large amount of unlabeled data. Our models automatically augment annotated data: (i) To improve the classification performance, and (ii) To capture the evolving picture of drug abuse on online social media. Our extensive experiment has been conducted on 3 million drug abuse-related tweets with geo-location information. Results show that our approach is highly effective in detecting drug abuse risk behaviors.

Original language	English (US)
Title of host publication	Computational Data and Social Networks - 7th International Conference, CSoNet 2018, Proceedings
Editors	My T. Thai, Xuemin Chen, Wei Wayne Li, Arunabha Sen
Publisher	Springer Verlag
Pages	330-342
Number of pages	13
ISBN (Print)	9783030046477
DOIs	https://doi.org/10.1007/978-3-030-04648-4_28
State	Published - 2018
Event	7th International Conference on Computational Data and Social Networks, CSoNet 2018 - Shanghai, China Duration: Dec 18 2018 → Dec 20 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11280 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	7th International Conference on Computational Data and Social Networks, CSoNet 2018
Country/Territory	China
City	Shanghai
Period	12/18/18 → 12/20/18

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Keywords

Deep learning
Drug abuse
Self-taught learning
Tweets

Access to Document

10.1007/978-3-030-04648-4_28

Cite this

Hu, H., Phan, N. H., Geller, J., Vo, H., Manasi, B., Huang, X., Di Lorio, S., Dinh, T., & Chun, S. A. (2018). Deep self-taught learning for detecting drug abuse risk behavior in tweets. In M. T. Thai, X. Chen, W. W. Li, & A. Sen (Eds.), Computational Data and Social Networks - 7th International Conference, CSoNet 2018, Proceedings (pp. 330-342). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11280 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-04648-4_28

Hu, Han ; Phan, Nhat Hai ; Geller, James et al. / Deep self-taught learning for detecting drug abuse risk behavior in tweets. Computational Data and Social Networks - 7th International Conference, CSoNet 2018, Proceedings. editor / My T. Thai ; Xuemin Chen ; Wei Wayne Li ; Arunabha Sen. Springer Verlag, 2018. pp. 330-342 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{4c77310145144216b12a72011dd4b668,

title = "Deep self-taught learning for detecting drug abuse risk behavior in tweets",

abstract = "Drug abuse continues to accelerate toward becoming the most severe public health problem in the United States. The ability to detect drug abuse risk behavior at a population scale, such as among the population of Twitter users, can help us to monitor the trend of drug-abuse incidents. Unfortunately, traditional methods do not effectively detect drug abuse risk behavior, given tweets. This is because: (1) Tweets usually are noisy and sparse; and (2) The availability of labeled data is limited. To address these challenging problems, we proposed a deep self-taught learning system to detect and monitor drug abuse risk behaviors in the Twitter sphere, by leveraging a large amount of unlabeled data. Our models automatically augment annotated data: (i) To improve the classification performance, and (ii) To capture the evolving picture of drug abuse on online social media. Our extensive experiment has been conducted on 3 million drug abuse-related tweets with geo-location information. Results show that our approach is highly effective in detecting drug abuse risk behaviors.",

keywords = "Deep learning, Drug abuse, Self-taught learning, Tweets",

author = "Han Hu and Phan, {Nhat Hai} and James Geller and Huy Vo and Bhole Manasi and Xueqi Huang and {Di Lorio}, Sophie and Thang Dinh and Chun, {Soon Ae}",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 7th International Conference on Computational Data and Social Networks, CSoNet 2018 ; Conference date: 18-12-2018 Through 20-12-2018",

year = "2018",

doi = "10.1007/978-3-030-04648-4_28",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "330--342",

editor = "Thai, {My T.} and Xuemin Chen and Li, {Wei Wayne} and Arunabha Sen",

booktitle = "Computational Data and Social Networks - 7th International Conference, CSoNet 2018, Proceedings",

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}

Hu, H, Phan, NH , Geller, J, Vo, H, Manasi, B, Huang, X, Di Lorio, S, Dinh, T & Chun, SA 2018, Deep self-taught learning for detecting drug abuse risk behavior in tweets. in MT Thai, X Chen, WW Li & A Sen (eds), Computational Data and Social Networks - 7th International Conference, CSoNet 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11280 LNCS, Springer Verlag, pp. 330-342, 7th International Conference on Computational Data and Social Networks, CSoNet 2018, Shanghai, China, 12/18/18. https://doi.org/10.1007/978-3-030-04648-4_28

Deep self-taught learning for detecting drug abuse risk behavior in tweets. / Hu, Han; Phan, Nhat Hai ; Geller, James et al.
Computational Data and Social Networks - 7th International Conference, CSoNet 2018, Proceedings. ed. / My T. Thai; Xuemin Chen; Wei Wayne Li; Arunabha Sen. Springer Verlag, 2018. p. 330-342 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11280 LNCS).

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

TY - GEN

T1 - Deep self-taught learning for detecting drug abuse risk behavior in tweets

AU - Hu, Han

AU - Phan, Nhat Hai

AU - Geller, James

AU - Vo, Huy

AU - Manasi, Bhole

AU - Huang, Xueqi

AU - Di Lorio, Sophie

AU - Dinh, Thang

AU - Chun, Soon Ae

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2018.

PY - 2018

Y1 - 2018

N2 - Drug abuse continues to accelerate toward becoming the most severe public health problem in the United States. The ability to detect drug abuse risk behavior at a population scale, such as among the population of Twitter users, can help us to monitor the trend of drug-abuse incidents. Unfortunately, traditional methods do not effectively detect drug abuse risk behavior, given tweets. This is because: (1) Tweets usually are noisy and sparse; and (2) The availability of labeled data is limited. To address these challenging problems, we proposed a deep self-taught learning system to detect and monitor drug abuse risk behaviors in the Twitter sphere, by leveraging a large amount of unlabeled data. Our models automatically augment annotated data: (i) To improve the classification performance, and (ii) To capture the evolving picture of drug abuse on online social media. Our extensive experiment has been conducted on 3 million drug abuse-related tweets with geo-location information. Results show that our approach is highly effective in detecting drug abuse risk behaviors.

AB - Drug abuse continues to accelerate toward becoming the most severe public health problem in the United States. The ability to detect drug abuse risk behavior at a population scale, such as among the population of Twitter users, can help us to monitor the trend of drug-abuse incidents. Unfortunately, traditional methods do not effectively detect drug abuse risk behavior, given tweets. This is because: (1) Tweets usually are noisy and sparse; and (2) The availability of labeled data is limited. To address these challenging problems, we proposed a deep self-taught learning system to detect and monitor drug abuse risk behaviors in the Twitter sphere, by leveraging a large amount of unlabeled data. Our models automatically augment annotated data: (i) To improve the classification performance, and (ii) To capture the evolving picture of drug abuse on online social media. Our extensive experiment has been conducted on 3 million drug abuse-related tweets with geo-location information. Results show that our approach is highly effective in detecting drug abuse risk behaviors.

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KW - Self-taught learning

KW - Tweets

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Computational Data and Social Networks - 7th International Conference, CSoNet 2018, Proceedings

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Hu H, Phan NH , Geller J, Vo H, Manasi B, Huang X et al. Deep self-taught learning for detecting drug abuse risk behavior in tweets. In Thai MT, Chen X, Li WW, Sen A, editors, Computational Data and Social Networks - 7th International Conference, CSoNet 2018, Proceedings. Springer Verlag. 2018. p. 330-342. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-04648-4_28

Deep self-taught learning for detecting drug abuse risk behavior in tweets

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