R. Daniulaityte, R. Carlson, R. Falck, D. Cameron, S. Perera, L. Chen, A. P. Sheth. 130(1-3): 241-244, 2013. ScienceDirect, [PMID 23201175]</ref> <ref>R. Daniulaityte, R. Carlson, R. Falck, D. Cameron, S. Perera, L. Chen, A. P. Sheth. A Web-Based Study of Self-Treatment of Opioid Withdrawal Symptoms with Loperamide. The College on Problems of Drug Dependence CPDD 2012, Palm Springs, CA USA, June 9-14, 2012.
Usha Lokala, Lamy, R. Daniulaityte, Amit Sheth, Ramzi W. Nahhas, Jason I. Roden, Shweta Yadav, and Robert G. Carlson. "Global trends, local harms: availability of fentanyl-type drugs on the dark web and accidental overdoses in Ohio." Computational and Mathematical Organization Theory (2019): 1-12. Ramnath Kumar, Shweta Yadav, Raminta Daniulaityte, Francois Lamy, Krishnaprasad Thirunarayan, Usha Lokala, and Amit Sheth. 2020. eDarkFind: Unsupervised Multi-view Learning for Sybil Account Detection In Proceedings of The Web Conference 2020 (WWW ’20), April 20–24, 2020, Taipei, Taiwan.ACM, New York, NY, USA,11pages. Lamy, F. R., Daniulaityte, R., Barratt, M. J., Lokala, U., Sheth, A., & Carlson, R. G. (2020). Listed for sale: Analyzing data on fentanyl, fentanyl analogs and other novel synthetic opioids on one crypto market Drug and Alcohol Dependence, 213, 108115. Usha Lokala, Raminta Daniulaityte, Francois Lamy, Manas Gaur, Krishnaprasad Thirunarayan, Ugur Kursuncu, Amit Sheth. DAO: An Ontology for Substance Use Epidemiology on Social Media and Dark Web, JMIR Preprints. 10/10/2020:24938
Raw SQI does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improvement. Used to examine the effect of events, e.g., school closure, business closure, unemployment, and lockdown on worsening mental health.
Public Health, Epidemiology, Addiction X Social Media & AI
Public Health, Epidemiology,
Addictions x Social Media & AI
ARC Talk [11 Nov 2020]
Amit Sheth, Artificial Intelligence Institute, U of South Carolina
[Main collaborators: Raminta Daniulaityte; Contributors: Usha Lokala, Ugur Kursuncu, Manas Gaur, Kaushik Roy]
Our Addiction work - Acknowledgements
PREDOSE - (NIDA) Grant No. R21 DA030571-01A1
eDrug Trends - (NIDA) Grant No. 5R01DA039454-02
eDark Trends - (NIDA) Grant No 1R21DA044518
BD Spoke - (NSF) Award No 1761969
PREDOSE - Prescription Drug abuse Online Surveillance and Epidemiology
To develop techniques to facilitate prescription drug abuse epidemiology, related to the
illicit use of pharmaceutical opioids
To capture the knowledge, attitudes, and behaviors of prescription drug abusers
Detection of non-medical use of pharmaceutical opioids (buprenorphine)
To determine spatio-temporal-thematic patterns and trends in pharmaceutical opioid
Outcome of PREDOSE
Loperamide Withdrawal Discovery
Our “loperamide discovery” discovery: "I Just Wanted to Tell You That Loperamide WILL
WORK": A Web-Based Study of Extra-Medical Use of Loperamide. Journal of Drug and
Alcohol Dependence. 2013.
The opioid addictions treatment drugs Buprenorphine and Methadone are commonly
prescribed for treatment of withdrawal symptoms. Our analysis of Web forums found that
Loperamide we widely used for a similar purpose by taking it in 10x-20x prescribed OTC
dosage. Three toxicology studies following this work led to FDA warning in 2016.
PREDOSE Wiki: http://wiki.aiisc.ai/index.php/PREDOSE
Semi-automated platform to identify emerging trends in cannabis and synthetic
cannabinoid use in the U.S cannabis and synthetic cannabinoid use.
To analyze characteristics of marijuana concentrate users, describe patterns and reasons
To identify factors associated with daily use of concentrates among U.S.-based cannabis
users recruited via a Twitter-based online survey
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.
Analyze social network characteristics and identify key influencers (opinions leaders) in
cannabis and synthetic cannabinoid-related discussions on Twitter
Outcome of eDrug Trends
" When they say weed causes depression, but it's your fav antidepressant": Knowledge-aware
Attention Framework for Relationship Extraction between Cannabis and Depression
'Time for dabs': Analyzing Twitter data on butane hash oil use
"Time for dabs": Analyzing Twitter data on marijuana concentrates across the U.S.
“When ‘Bad’ is ‘Good”: Identifying Personal Communication and Sentiment in Drug-Related
“Those edibles hit hard”: exploration of Twitter data on cannabis edibles in the U.S.
What's your Type?: Contextualized Classification of User Types in Marijuana-Related
Communications Using Compositional Multiview Embedding
eDrug Trends Wiki : http://wiki.aiisc.ai/index.php/EDrugTrends
To monitor Cryptomarkets to Identify Emerging Trends of Illicit Synthetic Opioids Use
Semi automated platform to monitor illicit online transactions of several illicit synthetic
opioids in dark web.
To design effective and responsive prevention and policies for public health professionals
Epidemiological surveillance by providing timely data regarding emerging substances and
To monitor Darknet supply and marketing trends over time.
Enhancing the capacities of early warning systems like NDEWS
Background : why study Opioids?
Unprecedented increases in opioid related
overdose mortality in U.S.
Fuelled by Illicit Fentanyl and other novel
Novel Synthetic Opioids include: Non-
Fentanyl Analogs (Ex: Carfentanil, acetyl
fentanyl, furanyl fentanyl)
Other Novel Synthetic Opioids (not chemical
structurally related to fentanyl, AH-7921, U-
47700 and U-49900). Variation in potency
and other pharmacological features.
Why Cryptomarkets as a source?
Increased reports about Novel
Synthetic Opioids being sold on
Cryptomarket data could be used
as a novel epidemiological
surveillance tool for early
identification of emerging patterns
eDarkTrends project sample outcomes
Global trends, local harms: availability of fentanyl-type drugs on the dark web and
accidental overdoses in Ohio
eDarkFind: Unsupervised Multi-view Learning for Sybil Account Detection
Listed for sale: analyzing data on fentanyl, fentanyl analogs and other novel synthetic
opioids on one cryptomarket
DAO: An Ontology for Substance Use Epidemiology on Social Media and Dark Web
Public Health Addictions Wiki Page:
BD Spoke: Opioid and Substance Abuse in Ohio
● The opioid epidemic entrenched in
Ohio and the Midwest of the US.
● The prevalence of opioid and its
impact on the well-being of individuals
and the society in Ohio.
○ Mental Health & Suicide Risk
1. How can we use social media to measure
mental health impact of opioid
1. Are there association between opioid and
mental health/suicide risk based on social
Monitoring the prevalence of opioid and its impact on mental health and suicide in Ohio,
utilizing a scalable knowledge and data driven BIGDATA (BD) approach via social media.
BD Spoke: Approach Overview
● Sheth, Amit, and Pavan Kapanipathi. "Semantic filtering for social data." IEEE Internet Computing 20, no. 4 (2016): 74-78.
● Hussein S. Al-Olimat, Krishnaprasad Thirunarayan, Valerie Shalin, and Amit Sheth. 2018. Location Name Extraction from Targeted Text Streams using Gazetteer-based Statistical Language
Models. In Proceedings of the 27th International Conference on Computational Linguistics (COLING2018), pages 1986-1997. Association for Computational Linguistics
● Gaur, M., Kursuncu, U., Alambo, A., Sheth, A., Daniulaityte, R., Thirunarayan, K., & Pathak, J. (2018, October). " Let Me Tell You About Your Mental Health!" Contextualized Classification of Reddit
Posts to DSM-5 for Web-based Intervention. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (pp. 753-762).
● Gaur, M., Alambo, A., Sain, J. P., Kursuncu, U., Thirunarayan, K., Kavuluru, R., ... & Pathak, J. (2019, May). Knowledge-aware assessment of severity of suicide risk for early intervention. In The
World Wide Web Conference (pp. 514-525).
● Yazdavar, A. H., Al-Olimat, H. S., Ebrahimi, M., Bajaj, G., Banerjee, T., Thirunarayan, K., ... & Sheth, A. (2017, July). Semi-supervised approach to monitoring clinical depressive symptoms in social
media. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 (pp. 1191-1198).
● Daniulaityte, R., Nahhas, R. W., Wijeratne, S., Carlson, R. G., Lamy, F. R., Martins, S. S., ... & Sheth, A. (2015). “Time for dabs”: Analyzing Twitter data on marijuana concentrates across the US.
Drug and alcohol dependence, 155, 307-311.
Outcome of BD Spoke
Usha Lokala, Francois R. Lamy, Raminta Daniulaityte, Amit Sheth, Ramzi W. Nahhas, Jason I. Roden,
Shweta Yadav, and Robert G. Carlson. "Global trends, local harms: availability of fentanyl-type drugs on
the dark web and accidental overdoses in Ohio." Computational and Mathematical Organization Theory
25, no. 1 (2019): 48-59.
For information: http://wiki.aiisc.ai/index.php/Community-
Research has been utilized for understanding the increase in addiction and mental
health challenges during COVID-19.
● The Conversation: “We’re measuring online conversation to track the social and mental health
issues surfacing during the coronavirus pandemic”
● Healthline: “What Your Social Media Posts Say About Your Stress Level Right Now”
● Lightning Talk at Computing Research Association: “Psychidemic: Measuring the Spatio-Temporal
Psychological Impact of Novel Coronavirus”
Psychidemic: Mental health & Addiction during COVID-19
States show different patterns
on mental health and
For the states; OH, OR, IN, WY,
NH, WA, KS, social well-being
is going worse.
in OH, OR, IN, WY, NH, WA, KSFor information: wiki.aiisc.ai/index.php/Covid19
Collaborators on NIDA funded projects
Other PIs and Co-Investigators: Prof. Raminta Daniulaityte, Dr. Francois Lamy, Prof.
Robert Carlson, Prof. Krishnaprasad Thirunarayan, Prof. Ramzi Nahhas, Prof. Silvia
Martins (Columbia), Prof. Edward W. Boyer (UMass)
Postdoctoral Researchers: Dr. Ugur Kursuncu
Graduate Students: Usha Lokala
College of Health Solutions
Arizona State University
● Reddit usage has been skyrocketed since the coronavirus outbreak
● Less than 2 months → Coronavirus subreddit moved from 2000 to 2 Million subscribers
● Presently, there are 64 Moderators and growing (Thanks to Emerson Ailidh Boggs, UPitt)
○ Ph.D. and Masters in genomic science, infectious diseases, virology, and Tuberculosis
○ Nurses, General Practitioners, and Internal medicine specialist
○ Epidemiologists, and mental healthcare providers
○ Migration to COVID19_support subreddit - setup to support mental health concerns on
A careful inspection of COVID-19 on Reddit
Peer-support in online communities and social media
- Reading book
- Vitamin D
- Balanced diet
- Sleep cycle
-See a therapist
- Skype/Facetime call
May I ask how long we’re supposed to stay home and quarantine
ourselves? I’m beginning to feel depressed from being inside for long
When a user posts a comment
The goal is to match them to
the set of most helpful support
providers that can assist their
This example is for illustration
of general depression, but plan
is also to do the same for
Matching support seekers and support providers on reddit
Computational Approach - Matching across sub-reddits
Problem: I am not sleeping
much anymore. Anxiety is
pretty high for the stability
of the world and the future
of trust. Probably need to
take up drinking or
NLI Supportive, User 69XXX420BLAZIT, : Giving up is in your control.
Exercise can be lots of different things and a way to help anxiety.
NLI Similar Problem, User FlowJock : It is quite anxiety inducing. Maybe a
good time to learn some relaxation techniques
NLI Informative, User dooblyd : I hear you. Myself and other friends with
kids are going through similar anxiety right now. This is a rough time you
are not alone and I hope you can manage your stress
having already needed anti
anxiety meds, to be brutally
honest i would rather take
the chance to get corona
than feeling like my mind
was a warzone.
NLI Supportive, User xxiwisk :
Takes anti anxiety medicine
Medicine suppresses your immune
system .Doesnt take anti anxiety
medicine Anxiety suppresses your
NLI Informative, User Spuds1968, :
That is a sure fire way to spread
panic and anxiety. Do we have
hospitals all over the US with people
requiring respirators We have to
remain calm and not spread fears.
NLI Informative,User :
UnluckyOrganization, : Does anyone
else get waves of anxiety where you
think its pointless to keep on doing
your daily errands because well be
dead soon Lol
NLI Supportive,User giftedbribes,
: My point is anxiety is worse
than death so just go about your
day. Do what you can and the rest
is out of your control.
Evaluation using Natural Language Inference (NLI)
We planned to evaluate by
1. Show expert the
suggestions by the
algorithm to the support
seeker user, as can be
on the left
1. A support seeker’s
(problem user) post, is
matched with a maximum
of 8 support providers from
among the responses to
the reddit post.
2. The provider responses
picked could be
informative or supportive.
3. The algorithm classifies
them as such.
Support Providers matched to Support Seeker (example)
Next, ask them a series of questions that
would collectively help determine the
“score” of the algorithm in forming
quality matches (as shown in the last
We need help with better strategies for
evaluation as per expert suggestions that
1. The quality of the matches performed
by the algorithm
2. Consistency in confidence across a
range of experts
What other criteria would one
expect to see from an AI like
this to trust it to provide peer
support help online?
How to quantify quality of algorithm identified matches?
1. The study requires the recovery coach to provide advice in the form of feedback to
prevent the possibility of harmful recommendation.
2. Within the category of support seekers, there can be various sub-categories
reflecting the type of support. We require help from recovery coach in creating a
lexicon of category which can guide the classification of users
3. Categorize the post as: emotional support, informational support, encouragement,
appreciation, and empathy
4. If it possible to receive anonymized conversational transcripts of the patient so that
we can conduct a controlled study before experimenting at observational level on
5. Qualitative questions - Any medical knowledge that they would expect the AI to
What we believe we can expect from domain experts
We acknowledge full support from the (NIH) Grant No. R21 DA030571-01A1: A
Study of Social Web Data on Buprenorphine Abuse using Semantic Web Technology;
National Institute on Drug Abuse (NIDA) Grant No. 5R01DA039454-02: Trending:
Social media analysis to monitor cannabis and synthetic cannabinoid use and NIDA
Grant No 1R21DA044518: eDarkTrends: Monitoring Cryptomarkets to Identify
Emerging Trends of Illicit Synthetic Opioids Use. Any opinions, conclusions or
recommendations expressed in this material are those of the authors and do not
necessarily reflect the views of the NIH, or NIDA.