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Public Health, Epidemiology, Addiction X Social Media & AI

Review of Addiction related interdisciplinary and translational research at the AI Institute, focusing on using AI techniques on a broad variety of social media data for analysis and insights.

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Public Health, Epidemiology, Addiction X Social Media & AI

  1. 1. Public Health, Epidemiology, Addictions x Social Media & AI Artificial Intelligence Institute ARC Talk 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]
  2. 2. Our Addiction work - Acknowledgements 2 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
  3. 3. 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 abuse
  4. 4. PREDOSE Pipeline
  5. 5. Semantic extraction/annotation
  6. 6. 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
  7. 7. eDrug Trends 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 of use. 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
  8. 8. eDrug Trends Architecture
  9. 9. 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 Tweets. “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
  10. 10. eDark Trends 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 product form To monitor Darknet supply and marketing trends over time. Enhancing the capacities of early warning systems like NDEWS
  11. 11. eDark Trends Architecture
  12. 12. Background : why study Opioids? Unprecedented increases in opioid related overdose mortality in U.S. Fuelled by Illicit Fentanyl and other novel synthetic Opioids Novel Synthetic Opioids include: Non- Pharmaceutical Fentanyl 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.
  13. 13. Why Cryptomarkets as a source? Increased reports about Novel Synthetic Opioids being sold on cryptomarkets. Cryptomarket data could be used as a novel epidemiological surveillance tool for early identification of emerging patterns and trends.
  14. 14. 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: http://wiki.aiisc.ai/index.php/Public_Health_Addictions_Research_at_AIISC
  15. 15. BD Spoke: Opioid and Substance Abuse in Ohio Motivation ● 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 Questions 1. How can we use social media to measure mental health impact of opioid prevalence? 1. Are there association between opioid and mental health/suicide risk based on social media data? Approach 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.
  16. 16. BD Spoke: Approach Overview ScoreCalculation Opioid Mental Health Depression Addiction Suicide Risk Ideation, Behavior Attempt Correlations ● 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. News Articles Twitter Data Domain Knowledg e Content Enrichment DAO DSM-5 Location Extraction Keyphrase Extraction Age-based Clustering Semantic FilteringEntityExtraction NLM Training f(.) Knowledge Infused Natural Language Processing (Ki-NLP) Semantic Mapping Semantic Proximity Topic Model Language Model DAO DSM-5 Dashboard Visualizations (Online) Offline Analysis & Visualizations
  17. 17. 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- Driven_Data_Engineering_for_Substance_Abuse_Prevention_in_the_Rural_Midwest 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”
  18. 18. Psychidemic: Mental health & Addiction during COVID-19 SQI Declining.. Frequency Depression: 88491 Addiction: 24373 Anxiety: 37725 Total: 146589 Frequency Depression: 123244 Addiction: 84879 Anxiety: 94999 Total: 303122 States show different patterns on mental health and addiction. 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
  19. 19. 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 Assoc Professor Arizona State University Raminta.Daniulaityte@asu.edu
  20. 20. ● 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 Coronavirus subreddit. A careful inspection of COVID-19 on Reddit
  21. 21. Peer-support in online communities and social media
  22. 22. -Quarantine - Asthma - Reading book - Diversion -Meditate - Yoga - Vitamin D - Balanced diet - Trauma - Hygiene - Sleep cycle - Telehealth -Relationship -Walk -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 on reddit, The goal is to match them to the set of most helpful support providers that can assist their inquiry This example is for illustration of general depression, but plan is also to do the same for Addiction Matching support seekers and support providers on reddit
  23. 23. Problem Domain Specific Filtering Support Classifier Psycho linguistic Analysis Language Features Emotional Features Convolutional Autoencoder Clustering With Divergence Loss r/Coronavirus, r/Selfharm, r/Opiates r/COVID_support, r/StopSelfharm, r/OpiatesRecovery Computational Approach - Matching across sub-reddits
  24. 24. User:f0rkz 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 something… 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 User:CommunistWaterbottle 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 immune system 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)
  25. 25. We planned to evaluate by different experts, 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)
  26. 26. 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 slide). We need help with better strategies for evaluation as per expert suggestions that can show 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?
  27. 27. 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 Reddit 5. Qualitative questions - Any medical knowledge that they would expect the AI to demonstrate What we believe we can expect from domain experts
  28. 28. http://aiisc.ai/ 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.

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