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

Simple item page
dc.contributor.authorRoy, Arpita
dc.contributor.authorPaul, Anamika
dc.contributor.authorPirsiavash, Hamed
dc.contributor.authorPan, Shimei
dc.date.accessioned2018-03-09T13:49:35Z
dc.date.available2018-03-09T13:49:35Z
dc.date.issued2017
dc.descriptionPresented at the 2017 International Conference on Tools for Artificial Intelligence (ICTAI)en_US
dc.description.abstractAbstract—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.en_US
dc.description.urihttps://www.csee.umbc.edu/~hpirsiav/papers/substance_ictai17.pdfen_US
dc.format.extent8 pagesen_US
dc.genreconference papers and proceedingsen_US
dc.identifierdoi:10.13016/M2416T178
dc.identifier.urihttp://hdl.handle.net/11603/7849
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.haspartUMBC Student Collection
dc.relation.haspartUMBC Faculty Collection
dc.relation.haspartUMBC Information Systems Department
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.rightsThis 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.
dc.subjectsocial mediaen_US
dc.subjectsubstance useen_US
dc.subjectillicit drugsen_US
dc.subjectteensen_US
dc.subjectneural networken_US
dc.subjectconvolutional neural networken_US
dc.subjectdocument embeddingen_US
dc.titleAutomated Detection of Substance Use-Related Social Media Posts Based on Image and Text Analysisen_US
dc.typeTexten_US

Files

License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.68 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

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