Cannabis remains one of the most commonly used psychoactive substances in the U.S., and current changes in legalization policy indicate broadening acceptability. At the same time, substance abusers have sought similar, or more enhanced highs, through use of synthetic cannabinoids, new designer drugs with constantly changing chemical formulations that have been linked to adverse health effects and present significant challenges to public health. Submitted in response to NIDA RFA-CA-14-008 (Research Area 1), this multi-PI study builds on multidisciplinary collaboration between the researchers in the Center for Interventions, Treatment, and Addictions Research, and the Ohio Center of Excellence in Knowledge-enabled Computing at Wright State University to develop an innovative software platform, eDrugTrends, that will facilitate analysis of trends in knowledge, attitudes, and behaviors related to the use of cannabis and synthetic cannabinoids as discussed on Web forums and Twitter. To date, no one has developed a computer platform capable of collecting and semi-automatically analyzing data from these two complementary social media sources. We will build on the existing infrastructure developed by our interdisciplinary research team: Twitris, a robust and highly scalable platform designed to analyze Twitter data, and PREDOSE, a platform developed for our collaborative NIH grant (R21 DA030571) to analyze Web forum data on illegal buprenorphine use. Key elements of Twitris and PREDOSE will be adapted and enhanced using Semantic Web, Natural Language Processing, and Machine Learning techniques to advance the analysis of social media data for drug abuse research and is thus maximally responsive to the RFA.
The Specific Aims are to: 1) Develop a comprehensive software platform, eDrugTrends, for semi-automated processing and visualization of thematic, sentiment, spatio-temporal, and social network dimensions of social media data (Twitter and Web forums) on cannabis and synthetic cannabinoid use. 2) Deploy eDrugTrends to: a) Identify and compare trends in knowledge, attitudes, and behaviors related to cannabis and synthetic cannabinoid use across U.S. regions with different cannabis legalization policies using Twitter and Web forum data. b) Analyze social network characteristics and identify key influencers (opinions leaders) in cannabis and synthetic cannabinoid-related discussions on Twitter. The development of eDrugTrends will advance the field's technological and methodological capabilities, and our deployment of the platform will inform the field on new trends regarding the use of cannabis, synthetic cannabinoids and other drugs. eDrugTrends will have high public health impact by providing a tool that can be used to inform more timely interventions and policy responses to changes in cannabis and synthetic cannabinoid use and associated harms.
Building on inter-disciplinary collaboration between substance abuse researchers and computer scientists, we will develop and deploy an innovative software platform, eDrugTrends, to facilitate analysis of Twitter and Web forum data on cannabis and synthetic cannabinoid use. The study will contribute to the advancement of social media surveillance methods to improve public health, and it will provide timely information on emerging trends in the context of changing cannabis laws in the U.S. that can ultimately be used to inform interventions and policy.
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| Lamy, Francois R; Daniulaityte, Raminta; Zatreh, Mussah et al. (2018) ""You got to love rosin: Solventless dabs, pure, clean, natural medicine."" Exploring Twitter data on emerging trends in Rosin Tech marijuana concentrates. Drug Alcohol Depend 183:248-252 |
| Perlmutter, Alexander S; Conner, Sarah C; Savone, Mirko et al. (2017) Is employment status in adults over 25 years old associated with nonmedical prescription opioid and stimulant use? Soc Psychiatry Psychiatr Epidemiol 52:291-298 |
| Lamy, Francois R; Daniulaityte, Raminta; Nahhas, Ramzi W et al. (2017) Increases in synthetic cannabinoids-related harms: Results from a longitudinal web-based content analysis. Int J Drug Policy 44:121-129 |
| Daniulaityte, Raminta; Lamy, Francois R; Barratt, Monica et al. (2017) Characterizing marijuana concentrate users: A web-based survey. Drug Alcohol Depend 178:399-407 |
| Daniulaityte, Raminta; Lamy, Francois R; Smith, G Alan et al. (2017) ""Retweet to Pass the Blunt"": Analyzing Geographic and Content Features of Cannabis-Related Tweeting Across the United States. J Stud Alcohol Drugs 78:910-915 |
| Daniulaityte, Raminta; Chen, Lu; Lamy, Francois R et al. (2016) ""When 'Bad' is 'Good'"": Identifying Personal Communication and Sentiment in Drug-Related Tweets. JMIR Public Health Surveill 2:e162 |
| Lamy, Francois R; Daniulaityte, Raminta; Sheth, Amit et al. (2016) ""Those edibles hit hard"": Exploration of Twitter data on cannabis edibles in the U.S. Drug Alcohol Depend 164:64-70 |
| Wijeratne, Sanjaya; Balasuriya, Lakshika; Sheth, Amit et al. (2016) EmojiNet: Building a Machine Readable Sense Inventory for Emoji. Proc Int Workshop Soc Inform 10046:527-541 |
| Daniulaityte, Raminta; Nahhas, Ramzi W; Wijeratne, Sanjaya et al. (2015) ""Time for dabs"": Analyzing Twitter data on marijuana concentrates across the U.S. Drug Alcohol Depend 155:307-11 |
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