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

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dc.contributor.authorRoy, Arpita
dc.contributor.authorPaul, Anamika
dc.contributor.authorPirsiavash, Hamed
dc.contributor.authorPan, Shimei
dc.date.accessioned2019-07-03T17:31:45Z
dc.date.available2019-07-03T17:31:45Z
dc.date.issued2018-06-07
dc.description2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI).en_US
dc.description.abstractNowadays, 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://ieeexplore.ieee.org/abstract/document/8372025en_US
dc.format.extent8 pagesen_US
dc.genreconference papers and proceedings preprintsen_US
dc.identifierdoi:10.13016/m2nywr-a9qk
dc.identifier.citationArpita Roy, et.al, Automated Detection of Substance Use-Related Social Media Posts Based on Image and Text Analysis, 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), DOI: 10.1109/ICTAI.2017.00122en_US
dc.identifier.urihttps://doi.org/10.1109/ICTAI.2017.00122
dc.identifier.urihttp://hdl.handle.net/11603/14341
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rights© 2017 IEEE
dc.subjectsocial mediaen_US
dc.subjectsubstance useen_US
dc.subjectillicit drugen_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

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