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Public Health, Epidemiology, Addictions x Social Media & AI
Current Work
With the increasing legalization of medical and recreational use of
substances, more research is needed to understand the association between
mental health and user behavior related to drug consumption. Specifically,
drug overdose and substance use-related mental health issues have become
two major topics that have been widely discussed on social media platforms.
Big social media data has the potential to provide deeper insights about these
associations to public health analysts for making policy decisions. Multiple
national population surveys have found that about half of those who
experience a mental health illness during their lives will also experience a
substance use disorder and vice versa. The communications related to
addiction and mental health are complex to process and understand given
their language and contextual characteristics. Surface-level data analysis alone
is not sufficient to understand the complex nature of relationships among the
addiction and mental health context. Moreover, dark web vendors have been
using social media as a new marketplace for drugs. Social media users also
discuss the novel drugs emerging in dark web marketplaces and associated side
effects/health conditions. These communications get complex when
researchers try to annotate them or link them to a specific mental health
entity. Considering the significant sensitivity of such communications and to
protect user privacy on social media, a potential solution requires reliable
algorithms for modeling such communications. We demonstrate the value of
incorporating domain-specific knowledge in natural language understanding to
identify the relationship between mental health and drug addiction. We
discuss end-to-end knowledge-infused deep learning frameworks that leverage
the pre-trained language representation model and domain-specific
declarative knowledge source to extract entities and their relationships jointly.
Our model is further tailored to focus on the entities mentioned in the
sentence where ontology is used to locate the target entity's position. We also
demonstrate the capabilities of inclusion of the knowledge-aware
representation in association with language models that can extract the
Drug-Mental health condition associations.
Publications (use Google Scholar to get details)
1. "When they say weed causes depression, but it's your fav antidepressant":
Knowledge-aware Attention Framework for Relationship Extraction between
Cannabis and Depression. PLOS ONE
2. 'Time for dabs': Analyzing Twitter data on butane hash oil use. Drug and
Alcohol Dependence
3. "Time for dabs": Analyzing Twitter data on marijuana concentrates across the
U.S. Drug and Alcohol Dependence
4. “When ‘Bad’ is ‘Good”: Identifying Personal Communication and Sentiment in
Drug-Related Tweets. JMIR
5. “Those edibles hit hard”: exploration of Twitter data on cannabis edibles in the
U.S. Drug and Alcohol Dependence
6. What's your Type?: Contextualized Classification of User Types in
Marijuana-Related Communications Using Compositional Multiview
Embedding. Web Intelligence
7. Global trends, local harms: availability of fentanyl-type drugs on the dark web
and accidental overdoses in Ohio. Computational and Mathematical
Organization Theory
Acknowledgements: This study was supported by the National Institute of Health (NIH) and
National Institute on Drug Abuse (NIDA), Grant No. 1 R2 1 DA0445 1 8 (Daniulaityte, PI;
Sheth, PI; Lamy, PI).
Conclusion
The study done here is highly innovative as this is the first
study to track and describe trends in illicit synthetic opioid supply
on two crypto markets for epidemiological monitoring in
Cincinnati and Montgomery County. The crawlers are created for
U.S-focused crypto market tracking of illicit synthetic opioids
supply and availability trends, and we made use of NER and
drug abuse ontology to extract all the relevant drug names from
the product names mentioned in crypto markets. In future, we
intend to implement the automation of all the tasks discussed
here from end to end process without much manual intervention.
This helps us to do the crypto market research much efficiently to
identify new emerging products automatically and explore
several drug forms and combinations.
Table 1: Dream Market Products Statistics (03/22/2018 - 04/26/2018)
Time
Period
No of
Ads
Avg all
Opioids
Avg
Fentanyl
Avg Synth
Opioids
Avg Opiates
Ads
Mar-18 3804 3497 289 9 3199
Apr-18 4094 3479 283 23 3173
Table 2: Agora Market Products Statistics (03/01/2015 - 05/29/2015)
Time
Period
No of
Ads
Avg all
Opioids
Avg
Fentanyl
Avg Synth
Opioids
Avg Opiates
Ads
Mar-18 21389 1045 170 19 856
Apr-18 21624 1313 203 16 1094
May-18 21840 1378 183 20 1175
Amit Sheth, Raminta Daniulaityte, Francois Lamy, Robert Carlson, Usha Lokala, Ugur Kursuncu, Manas Gaur
Cannabis Study eDrug Trends Wiki : http://wiki.aiisc.ai/index.php/EDrugTrends
Public Health Addictions Wiki : http://wiki.aiisc.ai/index.php/Public_Health_Addictions_Research_at_AIISC
8. eDarkFind: Unsupervised Multi-view Learning for Sybil Account Detection.
WWW
9. Listed for sale: analyzing data on fentanyl, fentanyl analogs and other novel
synthetic opioids on one cryptomarket. Drug and Alcohol Dependence
10. DAO: An Ontology for Substance Use Epidemiology on Social Media and Dark
Web. JMIR
11. eDarkTrends: Harnessing Social Media Trends in Substance Use Disorders for
Opioid Listings on Cryptomarket." ICLR AI for Public Health Workshop 2021. ICLR AI
for Public Health.
Research

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AIISC Public Health, Epidemiology, Addictions x Social Media & AI

  • 1. Public Health, Epidemiology, Addictions x Social Media & AI Current Work With the increasing legalization of medical and recreational use of substances, more research is needed to understand the association between mental health and user behavior related to drug consumption. Specifically, drug overdose and substance use-related mental health issues have become two major topics that have been widely discussed on social media platforms. Big social media data has the potential to provide deeper insights about these associations to public health analysts for making policy decisions. Multiple national population surveys have found that about half of those who experience a mental health illness during their lives will also experience a substance use disorder and vice versa. The communications related to addiction and mental health are complex to process and understand given their language and contextual characteristics. Surface-level data analysis alone is not sufficient to understand the complex nature of relationships among the addiction and mental health context. Moreover, dark web vendors have been using social media as a new marketplace for drugs. Social media users also discuss the novel drugs emerging in dark web marketplaces and associated side effects/health conditions. These communications get complex when researchers try to annotate them or link them to a specific mental health entity. Considering the significant sensitivity of such communications and to protect user privacy on social media, a potential solution requires reliable algorithms for modeling such communications. We demonstrate the value of incorporating domain-specific knowledge in natural language understanding to identify the relationship between mental health and drug addiction. We discuss end-to-end knowledge-infused deep learning frameworks that leverage the pre-trained language representation model and domain-specific declarative knowledge source to extract entities and their relationships jointly. Our model is further tailored to focus on the entities mentioned in the sentence where ontology is used to locate the target entity's position. We also demonstrate the capabilities of inclusion of the knowledge-aware representation in association with language models that can extract the Drug-Mental health condition associations. Publications (use Google Scholar to get details) 1. "When they say weed causes depression, but it's your fav antidepressant": Knowledge-aware Attention Framework for Relationship Extraction between Cannabis and Depression. PLOS ONE 2. 'Time for dabs': Analyzing Twitter data on butane hash oil use. Drug and Alcohol Dependence 3. "Time for dabs": Analyzing Twitter data on marijuana concentrates across the U.S. Drug and Alcohol Dependence 4. “When ‘Bad’ is ‘Good”: Identifying Personal Communication and Sentiment in Drug-Related Tweets. JMIR 5. “Those edibles hit hard”: exploration of Twitter data on cannabis edibles in the U.S. Drug and Alcohol Dependence 6. What's your Type?: Contextualized Classification of User Types in Marijuana-Related Communications Using Compositional Multiview Embedding. Web Intelligence 7. Global trends, local harms: availability of fentanyl-type drugs on the dark web and accidental overdoses in Ohio. Computational and Mathematical Organization Theory Acknowledgements: This study was supported by the National Institute of Health (NIH) and National Institute on Drug Abuse (NIDA), Grant No. 1 R2 1 DA0445 1 8 (Daniulaityte, PI; Sheth, PI; Lamy, PI). Conclusion The study done here is highly innovative as this is the first study to track and describe trends in illicit synthetic opioid supply on two crypto markets for epidemiological monitoring in Cincinnati and Montgomery County. The crawlers are created for U.S-focused crypto market tracking of illicit synthetic opioids supply and availability trends, and we made use of NER and drug abuse ontology to extract all the relevant drug names from the product names mentioned in crypto markets. In future, we intend to implement the automation of all the tasks discussed here from end to end process without much manual intervention. This helps us to do the crypto market research much efficiently to identify new emerging products automatically and explore several drug forms and combinations. Table 1: Dream Market Products Statistics (03/22/2018 - 04/26/2018) Time Period No of Ads Avg all Opioids Avg Fentanyl Avg Synth Opioids Avg Opiates Ads Mar-18 3804 3497 289 9 3199 Apr-18 4094 3479 283 23 3173 Table 2: Agora Market Products Statistics (03/01/2015 - 05/29/2015) Time Period No of Ads Avg all Opioids Avg Fentanyl Avg Synth Opioids Avg Opiates Ads Mar-18 21389 1045 170 19 856 Apr-18 21624 1313 203 16 1094 May-18 21840 1378 183 20 1175 Amit Sheth, Raminta Daniulaityte, Francois Lamy, Robert Carlson, Usha Lokala, Ugur Kursuncu, Manas Gaur Cannabis Study eDrug Trends Wiki : http://wiki.aiisc.ai/index.php/EDrugTrends Public Health Addictions Wiki : http://wiki.aiisc.ai/index.php/Public_Health_Addictions_Research_at_AIISC 8. eDarkFind: Unsupervised Multi-view Learning for Sybil Account Detection. WWW 9. Listed for sale: analyzing data on fentanyl, fentanyl analogs and other novel synthetic opioids on one cryptomarket. Drug and Alcohol Dependence 10. DAO: An Ontology for Substance Use Epidemiology on Social Media and Dark Web. JMIR 11. eDarkTrends: Harnessing Social Media Trends in Substance Use Disorders for Opioid Listings on Cryptomarket." ICLR AI for Public Health Workshop 2021. ICLR AI for Public Health. Research