An insight analysis and detection of drug-abuse risk behavior on Twitter with self-taught deep learning

Document Type

Article

Publication Date

12-1-2019

Abstract

Drug abuse continues to accelerate towards 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 propose 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 experiments have been conducted on three million drug-abuse-related tweets with geo-location information. Results show that our approach is highly effective in detecting drug-abuse risk behaviors.

Identifier

85074848372 (Scopus)

Publication Title

Computational Social Networks

External Full Text Location

https://doi.org/10.1186/s40649-019-0071-4

e-ISSN

21974314

Issue

1

Volume

6

Grant

CNS-1624503

Fund Ref

National Sleep Foundation

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