TY  - JOUR
PY  - 2017//
TI  - Machine learning to detect prescription opioid abuse promotion and access via Twitter
JO  - American journal of public health
A1  - Mackey, Tim K.
A1  - Kalyanam, Janani
A1  - Katsuki, Takeo
A1  - Lanckriet, Gert
SP  - 1910
EP  - 1915
VL  - 107
IS  - 12
N2  - OBJECTIVES: To deploy a methodology accurately identifying tweets marketing the illegal online sale of controlled substances. <br><br>METHODS: We first collected tweets from the Twitter public application program interface stream filtered for prescription opioid keywords. We then used unsupervised machine learning (specifically, topic modeling) to identify topics associated with illegal online marketing and sales. Finally, we conducted Web forensic analyses to characterize different types of online vendors. We analyzed 619 937 tweets containing the keywords codeine, Percocet, fentanyl, Vicodin, Oxycontin, oxycodone, and hydrocodone over a 5-month period from June to November 2015. <br><br>RESULTS: A total of 1778 tweets (< 1%) were identified as marketing the sale of controlled substances online; 90% had imbedded hyperlinks, but only 46 were "live" at the time of the evaluation. Seven distinct URLs linked to Web sites marketing or illegally selling controlled substances online. <br><br>CONCLUSIONS: Our methodology can identify illegal online sale of prescription opioids from large volumes of tweets. Our results indicate that controlled substances are trafficked online via different strategies and vendors. Public Health Implications. Our methodology can be used to identify illegal online sellers in criminal violation of the Ryan Haight Online Pharmacy Consumer Protection Act. (Am J Public Health. Published online ahead of print October 19, 2017: e1-e6. doi:10.2105/AJPH.2017.303994).<p />  <p>Language: en</p>
LA  - en
SN  - 0090-0036
UR  - http://dx.doi.org/10.2105/AJPH.2017.303994
ID  - ref1
ER  -