Automated Detection of Substance Use-Related Social Media Posts Based on Image and Text Analysis

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https://www.csee.umbc.edu/~hpirsiav/papers/substance_ictai17.pdf

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http://hdl.handle.net/11603/7849

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UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection
UMBC Information Systems Department
UMBC Student Collection

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Date

2017

Type of Work

conference papers and proceedings
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This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.

Subjects

social media
substance use
illicit drugs
teens
neural network
convolutional neural network
document embedding

Abstract

Abstract—Nowadays, teens and young adults spend a significant amount of time on social media. According to the national survey of American attitudes on substance abuse, American teens who spend time on social media sites are at increased risk of smoking, drinking and illicit drug use. Reducing teens’ exposure to substance use-related social media posts may help minimize their risk of future substance use and addiction. In this paper, we present a method for automated detection of substance userelated social media posts. With this technology, substance userelated content can be automatically filtered out from social media. To detect substance use related social media posts, we employ the state-of-the-art social media analytics that combines Neural Network-based image and text processing technologies. Our evaluation results demonstrate that image features derived using Convolutional Neural Network and textual features derived using neural document embedding are effective in identifying substance use-related social media posts.