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The corresponding video is at https://youtu.be/ztNHKLTHBrA AIISC conducts foundational and translational research in AI. In this talk, we review part of the AIISC's research in Social Good, Social Harm, and Public Health.
This talk was given to the UofSC College on Information and Communication.

Additional project details at http://wiki.aiisc.ai

The corresponding video is at https://youtu.be/ztNHKLTHBrA AIISC conducts foundational and translational research in AI. In this talk, we review part of the AIISC's research in Social Good, Social Harm, and Public Health.
This talk was given to the UofSC College on Information and Communication.

Additional project details at http://wiki.aiisc.ai

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AIISC’s research in Social Good/Social Harm, Public Health and Epidemiology

  1. 1. AIISC’s research in Social Good/Social Harm, Public Health and Epidemiology A review with the colleagues in the College of Information and Communications Amit Sheth with relevant #AIISC team 10 Mar 2021 http://aiisc.ai Amit Sheth Ugur Kursuncu Kaushik Roy Manas Gaur Usha Lokala Thilini Wijesiriwardene
  2. 2. “ 2 AIISC: foundational AI research and interdisciplinary/translational AI research
  3. 3. Translational Research Areas of relevance to College of I & C Social Harm ◎ Harassment on Social Media ◎ Toxic Content ◎ Extremism & Radicalization ◎ Disinformation ◎ Gender-based Violence Social Good ◎ Crisis Management Public Health & Epidemiology ◎ Mental Health/ Depression/Suicide ◎ Drug Abuse, Addiction ◎ Zika, COVID-19 epedemic Personalized Health ◎ Asthma, Mental Health, Hypertension, Diabetes, 3
  4. 4. Funded Multidisciplinary Projects [NIH, NSF, Other- past & current] ❖ Context-Aware Harassment Detection on Social Media ❖ eDarkTrends : Monitoring Cryptomarkets to Identify Emerging Trends of Illicit Synthetic Opioids Use ❖ Social media analysis to monitor cannabis and synthetic cannabinoid use ❖ Community-Driven Data Engineering for Substance Abuse Prevention in the Rural Midwest ❖ Modeling Social Behavior for Healthcare Utilization in Depression ❖ Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response ❖ KHealth: Semantic Multisensory Mobile Approach to Personalized Asthma Care ❖ Project Safe Neighborhood: Westwood Partnership to Prevent Juvenile Repeat Offenders ❖ SoCS: Social Media Enhanced Organizational Sensemaking in Emergency Response ❖ PREDOSE: PREscription Drug abuse Online Surveillance and Epidemiology 4
  5. 5. Sample Proposals indicating strategic direction [P=pending] ❏ Developing the Framework for a Comprehensive Multi-modal mHealth Tool designed to Assist Patients Suffering from Multiple Chronic Conditions [P] ❏ Narrative, Moral and Social Foundations of Social Cyber-Attack in Social Media (involving Shannon Bowen- Mass Communication, Matthew Brashears- Sociology, Mirta Galesic- Psychology, Fil Menczer, Brendan Nyhan-Government, Amit Sheth) ❏ Study of the Discripancies in Diagnosis and Treatment of Cardiovascular Disease Based on Sex and Gender to improve Women’s Health (with UCSF Medicine) [P] ❏ NYCConnect: A Digital Platform to Improve Access to Healthcare Services for Older Adults in New York City [with Cornell Medicine] ❏ Developing a Socio-Cognitive-Computational Approach for Understanding Persuasion through Misinformation and its Diffusion across Social Communications Platforms (Amit Almor, Nitin Agarwal, Huan Liu, Amit Sheth, Marco Valtorta, Douglas Wedell) 5
  6. 6. Research and Technology underpinning these efforts ★ Big Data (Volume, Variety- text/social media, images, video, sensors, multimodal data, Velocity, Veracity) ★ Artificial Intelligence: ○ Knowledge Graphs (domain models, vocabularies, taxonomies) ○ Natural Language Processing ○ Machine/Deep Learning 6
  7. 7. Other topics of possible interest Any challenging involving social media/news/literature big data ➔ SM platforms such as Twitter, Reddit, 4Chan ➔ multimodal (text, images,...) ➔ Large scale: 10s of million to billion+ tweets; millions of Reddit post ➔ analysis: spatio-temporal-thematic or content- ➔ people-network-sentiment-emotion-intention Applications: Election/public opinion prediction and analysis, Marketing, Branding (drivers of intentions and actions) 7
  8. 8. Biased Blackboxes: Data Has Inherent Bias that DL won’t find on its own--How to discover and mitigate? 8 Historical Bias Image Search: “CEO”. In 2018, 5% of Fortune 500 CEOs were women Representation Bias ImageNet Database Only 1% and 2.1% of the images come from China and India Measurement Bias Race Biased Crime Prediction Proxy variable “arrest” is often used to measure “crime” Aggregation Bias Predicting Health Complications Single model catering to different ethnicities is error prone Evaluation Bias Facial Recognition Only 7.4% & 4.4% share for dark skinned females in benchmark datasets such as Adience and IJB-A. Deployment Bias Risk Assessment Tools Crime prediction models used for predicting length of a sentence. And, its error prone. Suresh, Harini, and John V. Guttag. "A framework for understanding unintended consequences of machine learning." arXiv preprint arXiv:1901.10002 (2019).
  9. 9. 12 Social Good and Social Harm on Social Media A spectrum to demonstrate the variety of social good, social bad and social ugly. Adapted from : Purohit, Hemant & Pandey, Rahul. (2019). Intent Mining for the Good, Bad, and Ugly Use of Social Web: Concepts, Methods, and Challenges. 10.1007/978-3-319-94105-9_1. Help offering Expertise sharing Disaster Relief Joking Marketing Radicalization Rumouring Deceiving Accusing Sensationalizing Harassment Manipulation Social Good Social Ugly Social Bad Positive Effects Negative Effects
  10. 10. Context-Aware Harassment Detection on Social Media Severity of online harm can differ based on several criteria It can span for more than a decades in one’s life Or it can lead a teenager to suicide Visit Project Page: Context-Aware Harassment Detection on Social Media Supported by NSF Grants CNS 1513721
  11. 11. “Language used to express hatred towards a targeted individual or group, or is intended to be derogatory, to humiliate, or to insult the membe rs of the group, on the basis of attributes such as race, religion, ethnic origin, sexual orientation, disability, or gender is hate speech.” - Founta et al. 2018 Challenges in Online Harassment Detection Ambiguity According to Pew research center (2017) Researchers have defined harassment using jargon that overlaps, causing ambiguity in annotations “Profanity, strongly impolite, rude or vulgar language expressed with fighting or hurtful words in order to insult a targeted individual or group is offensive language ” - Founta et al. 2018 Subjectivity & Ex: @user_name nah you just a dumb #*! who doesn’t know her place 😂 😂 This tweet belongs to hate speech and offensive language based on above definitions
  12. 12. Challenges in Online Harassment Detection Sparsity Dataset # of Tweets Classes (%) Waseem et al. (2016) 16093 Racism (12%), Sexism (19.6%), Neither (68.4%) Davidson et al. (2017) 24,802 Hate (5%), Non- hate (95%) Zhang et al. (2018) 2,435 Hate (17%), Non-hate (83%) Mostly binary classifications Datasets have small percentages of positive (harassing) instances A Quality Type-aware Annotated Corpus And Lexicon For Harassment Research [Rezvan et al.] This paper provides both a quality annotated corpus and an offensive words lexicon capturing different types of harassment content: (i) sexual (ii) racial (iii) appearance-related (iv) intellectual (v) political
  13. 13. Analyzing and learning the language for different types of harassment Why and how conversations matter? Performance of multi-class classifier for predicting type of harassment incident Dataset Linguistic Analysis Statistical Analysis Type-aware classifier to identify type-specific harassment Using LIWC High ‘“female references” in the intellectual harassing corpus High occurrence of word “I” in sexually non-harassing corpus Analysis of unigrams Offensive words are commonplace in harassing and non- harassing corpora. Frequent words not necessarily offensive
  14. 14. ALONE: A Dataset for Toxic Behavior among Adolescents on Twitter Unique characteristics of the ALONE dataset: ● Data of Adolescent population ● Data organized based on interactions ● Multimodal Data ● Further insights: ○ Positive and negative lexical items in tweets used with contextual analysis of interactions suggest sarcasm and sometimes exonerate the potential toxic content. ○ Toxic interactions contain significantly high number of tweets per interaction along with a high number of multimodal elements such as images, videos and emojis.
  15. 15. Modeling Online Extremism using Knowledge-infused and Context-aware Learning Visit Project page: http://wiki.aiisc.ai/index.php/Modeling_Radicalization_on_Social_Media_using_Knowledge-infused_and_Context- aware_Learning Amit Sheth, Ugur Kursuncu, Vedant Khandelwal, Manas Gaur, Valerie L. Shalin (WSU), Dilshod Achilov (UMass-D), Krishnaprasad Thirunarayan (WSU), I. Budak Arpinar (UGA),
  16. 16. 20 “Reportedly, a number of apostates were killed in the process. Just because they like it I guess.. #SpringJihad #CountrysideCleanup” “Kindness is a language which the blind can see and the deaf can hear #MyJihad be kind always” “By the Lord of Muhammad (blessings and peace be upon him) The nation of Jihad and martyrdom can never be defeated” “Jihad” can appear in tweets with different meanings in different dimensions of the context. H I R Example Tweets with “Jihad”
  17. 17. 21 violence jihad god Deep Clustering Neural Parsing prayer jihad paradise killing Hate attack s violence Context understanding Shallow Semantics Deeper Semantics [Lin 2020, Kitaev 2018] https://github.com/facebookresearch/deepcluster [Kursuncu et al. 2018, 2019, 2020, 2021; Sheth et al. 2016, 2019; Gaur 2020] Social Media Posts (in millions) jihad paradise god prayer jihad jihad paradis e Knowledge How to Gain Deep Understanding of the Content?
  18. 18. 22 1. Kursuncu, U., Gaur, M., Castillo, C., Alambo, A., Thirunarayan, K., Shalin, V., ... & Sheth, A. (2019). Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions: Religion, Ideology, and Hate. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), 1-22. 2. Kursuncu, U., Purohit, H., Agarwal, N., & Sheth, A. (2020). When the Bad is Good and the Good is Bad: Understanding Cyber Social Health through Online Behavioral Change. Expert Systems with Applications, 2019, 1. Radicalization Process over time Modeling Modeling Modeling Dimension 1 Dimension 2 Dimension 3 Dimension Dimension Dimension Dimension Modeling Process Dimension based Knowledge enhanced Representation Non-extremist ordinary individual Radicalized extremist individual 0 1 2 4 Severe High Low None Elevated 3 ● Challenges vs. Opportunities ○ Context, false alarms, ambiguity, bias, transparency, multimodality ○ Incorporate knowledge in ML ● Dimensions: ○ Religion: Qur’an, Hadith, Qur’an Ontology ○ Ideology: Books, lectures of ideologues ○ Hate: Hate Speech Corpus (Davidson et al. 2017)
  19. 19. ● A group of extremist users, form a cluster farther from other users for Religion and Hate. ● Suggesting there might be outliers in the dataset. 23 Non-Extremist Context for Religion Extremist Context for Religion User Visualization for Dimensions Kursuncu, U., Gaur, M., Castillo, C., Alambo, A., Thirunarayan, K., Shalin, V., ... & Sheth, A. (2019). Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions: Religion, Ideology, and Hate. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), 1-22.
  20. 20. Public Health, Epidemiology, Addictions x Social Media & AI Visit Project page: http://wiki.aiisc.ai/index.php/ Amit Sheth, Raminta Daniulaityte (now ASU), Robert Carlson (WSU), Francois Lamy, Usha Lokala, Ugur Kursuncu
  21. 21. 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 25
  22. 22. PREDOSE: Prescription Drug abuse Online Surveillance and Epidemiology Visit Project page: http://wiki.aiisc.ai/index.php/PREDOSE Amit Sheth, Raminta Daniulaityte (now ASU), Robert Carlson (WSU), Delroy Cameron (now Apple), Pavan Kapanipathi (now IBM), Sujan Perera (now Amazon)
  23. 23. ● Facilitate Techniques for the illicit use of pharmaceutical opioids ● Capture the knowledge, attitudes, and behaviors of prescription drug abusers ● Detection of non-medical use of pharmaceutical opioids (buprenorphine) ● Determine spatio-temporal- thematic patterns and trends in pharmaceutical opioid abuse PREDOSE Pipeline
  24. 24. Semantic Extraction/Annotation
  25. 25. 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. Outcome of PREDOSE Loperamide Withdrawal Discovery
  26. 26. eDrug Trends: Trending Social Media Analysis to Monitor Cannabis and Synthetic Cannabinoid use Visit Project page: http://wiki.aiisc.ai/index.php/EDrugTrends Amit Sheth, Raminta Daniulaityte (now ASU), Francois R. Lamy, Robert Carlson (WSU), Krishnaprasad Thirunarayan (WSU), Ramzi Nahhas (WSU), Usha Lokala, Ugur Kursuncu,
  27. 27. What is eDrug Trends? Semi-automated platform to identify emerging trends in cannabis and synthetic cannabinoid use in the U.S. 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 31
  28. 28. eDrug Trends Pipeline 32
  29. 29. 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 " Is depression related to cannabis?": A knowledge-infused model for Entity and Relation Extraction with Limited Supervision '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 33
  30. 30. eDark Trends: Monitoring Cryptomarkets to Identify Emerging Trends of Illicit Synthetic Opioids Use Visit Project page: http://wiki.aiisc.ai/index.php/Public_Health_Addictions_Research_at_AIISC Amit Sheth, Francois R. Lamy, Raminta Daniulaityte (now ASU), Ramzi Nahhas (WSU), Ugur Kursuncu,
  31. 31. What is 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 35
  32. 32. eDark Trends Pipeline 36
  33. 33. Outcome of eDark Trends 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 39
  34. 34. BD Spokes: Community-Driven Data Engineering for Substance Abuse Prevention in the Rural Midwest Visit Project page: http://wiki.aiisc.ai/index.php/Community- Driven_Data_Engineering_for_Substance_Abuse_Prevention_in_the_Rural_Midwest Amit Sheth, Ugur Kursuncu, Usha Lokala, Goonmeet Bajaj (OSU), Ayaz Hyder (OSU)
  35. 35. 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. BD Spoke: Opioid and Substance Use in Ohio
  36. 36. Score Calculation 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 Filtering Entity Extraction 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 BD Spoke: Opioid and Substance Use in Ohio
  37. 37. ● Substance use addictive disorder linked to opioid with higher correlation. ● Gender dysphoria, Dissociative and OCD disorders are correlating moderately. Opioid Prevalence in Ohio vs. Mental Health & Suicide ● Suicide ideation (initial stage) with highest correlation. ● Mild severity level of suicide risk linked to higher correlation. ● Weak correlation for suicide indication (before initial) p N counties p N counties
  38. 38. Psychidemic: Measuring the Spatio-Temporal Psychological Impact of Novel Coronavirus Visit Project page: http://wiki.aiisc.ai/index.php/Covid19 Amit Sheth, Valerie L. Shalin (WSU), Ugur Kursuncu, Manas Gaur, Vedant Khandelwal, Vishal Pallagani
  39. 39. 47 More at: http://wiki.aiisc.ai/index.php/Covid19
  40. 40. A calculated Social Quality Index (SQI) aggregates mental health components (Depression, Anxiety), Addiction and Substance Use Disorders. vecteezy.com ● Change in SQI informs comparisons between states. ● Raw transformed SQI into relative state rankings changing over time. Social Quality Index (SQI)
  41. 41. Psychidemic: Mental health & Addiction during CO 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, KS For information: wiki.aiisc.ai/index.php/Covid19 Psychidemic: Mental health & Addiction during COVID-19
  42. 42. SQI bad SQI better SQI better SQI better Frequency Depression: 125037 Addiction: 92897 Anxiety: 81891 Total: 299825 Frequency Depression: 113830 Addiction: 81810 Anxiety: 74080 Total: 269720 Frequency Depression: 81463 Addiction: 60166 Anxiety: 45998 Total: 187627 Frequency Depression: 59088 Addiction: 49086 Anxiety: 46887 Total: 155061 IL, NY, MD, AZ, NM, MA. March 14-20 March 21-27 March 28-April 3 April 4-10 Reactions of States, --Improving SQI Ranking ● States have different circumstances, social, health, political. ● Mental health implications are also different.
  43. 43. SQI worse Cluster 4: CT, LA, NJ, NV, OK, RI, WI. SchoolClosures: CT, LA, NJ, NV,RI, WV, WI Business Closures: CT, LA,NJ, RI, WV, WI Social Distancing Reg: LA, NJ, RI, WV, WI Business Relief: WI Unemploymentincrease: CT 2.5K %, LA 2.5K %, NJ 1.2K %, NV 1.2K %, OK 1.2K %, RI 2.5K %, WI 1.2K %. Stay at home: CT, LA,NJ, OK, RI, WI, WV Extension School: CT, WV Major Disaster: NJ Business Relief: NJ Unemploymentincrease: CT 180%,LA 0 %, NJ 64 %, NV 0 %, OK 99 %, RI -23%, WI 99 %. Major Disaster: CT, WV Strict Social Dist: CT, RI Extensions deadlines: CT Medical shortage: NJ Extension Stay home: OK Extension School: RI Extension Business Closure: RI Business Relief: NJ, RI IndividualRelief: RI Unemploymentincrease: CT 0%, LA 5 %, NJ 3 %, NV 11 %, OK 7 %, RI 0%, WI -5 %. Extension School: CT Extension Stay home: LA Strict Social Dist: NJ Business Relief: WI Cluster 5: FL, GA, MI, NE, TN, VA, WV. SchoolClosures: FL, GA, MI, TN, VA, WV, Business Closures: WV, MI Social Distancing Reg: FL, MI, NE, TN, VA,WV, Business Relief: FL, GA, MI, NE, TN, VA IndividualRelief: TN, VA Unemploymentincrease: FL 600%,GA 650%,MI 180%, NE 70%, TN 180%,VA 180%, WV 600% Stay at home: MI, WV Shelterin Place: GA Business Closure: GA, TN Extension School: GA, WV Major Disaster: FL Business Relief: TN IndividualRelief: TN Unemploymentincrease: FL 3.1K%, GA 3K%, MI 1.8K%, NE 200%,TN 700%,VA 1.6K%, WV 1.7K% Stay at home: FL, VA Shelterin Place: TN Major Disaster: GA, MI, TN, VA,WV Strict Social Dist: GA Extension School: GA, MI Unemploymentincrease: FL -25%, GA 190%,MI 27%, NE 8%, TN 26%, VA 33%, WV 0% Extension School: GA Extension Stay home: MI SQI worse SQI worse SQI worse SQI better SQI better SQI better SQI better March 14-20 March 21-27 March 28-April 3 Influence of External Events
  44. 44. Modeling Social Behavior for Healthcare Utilization in Depression (NIMH R01) http://wiki.aiisc.ai/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression 52
  45. 45. Overview on Mental Health Research ◎ Understanding mental health from social media (Twitter, Reddit - at a massive scale; multimodal- text, images) and clinical (EMR) documents; also developing virtual agent (chatbot) ◎ Collaborations with domain experts/practitioners, Use of clinical knowledge (extracted from DSM-5) to enhance state-of-the-art deep learning and NLP/NLU ◎ Target users/audience/beneficiaries - patients, mental health experts, clinical researchers, policy makers 53
  46. 46. 54 Really struggling with my bisexuality which is causing chaos in my relationship with a girl. Being a fan of LGBTQ community, I am equal to worthless for her. I’m now starting to get drunk because I can’t cope with the obsessive, intrusive thoughts, and need to get it out of my head. Is mental health related ? Yes: 0.71 , No: 0.29 Which Mental Health condition? Predicted: Depression (False) True: Obsessive Compulsive Disorder Reasoning over Model: Why model predicted Depression? Unknown Manas Gaur, Ugur Kursuncu, Amanuel Alambo, Amit Sheth, Raminta Daniulaityte, Krishnaprasad Thirunarayan, and Jyotishman Pathak. "" Let Me Tell You About Your Mental Health!" Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention." CIKM 2018. [CIKM 2018]
  47. 47. 55 Really struggling with my bisexuality which is causing chaos in my relationship with a girl. Being a fan of XXX community, I am equal to worthless for her. I’m now starting to get drunk because I can’t cope with the obsessive, intrusive thoughts, and need to get out of my head. 288291000119102: High risk bisexual behavior 365949003: Health-related behavior finding 365949003: Health-related behavior finding 307077003: Feeling hopeless 365107007: level of mood 225445003: Intrusive thoughts 55956009: Disturbance in content of thought 26628009: Disturbance in thinking 1376001: Obsessive compulsive personality disorder Multi-hop traversal on Medical knowledge graphs <is symptom> Achieving Explainability through Medical Entity Normalization : Replacing Entities in the post with Concepts in the Medical Knowledge Graph through Semantic Annotation
  48. 48. 56 K-IL: Semi-Deep Infusion : Matching Reddit Conversation to DSM-5 Scenario Really struggling with my bisexuality which is causing chaos in my relationship with a girl. Being a fan of LGBTQ community, I am equal to worthless for her. I’m now starting to get drunk because I can’t cope with the obsessive, intrusive thoughts, and need to get out of my head. BPD DICD PND SAD SBI OCD Don’t want to live anymore. Sexually assault, ignorant family members and my never ending loneliness brights up my path to death. SCW PND SBI SAD DPR DICD DPR I do have a potential to live a decent life but not with people who abandon me. Hopelessness and feelings of betrayal have turned my nights to days. I am developing insomnia because of my restlessness. SBI DPR DICD BPD I just can’t take it anymore. Been abandoned yet again by someone I cared about. I've been diagnosed with borderline for a while, and I’m just going to isolate myself and sleep forever. SBI PND Reddit DSM-5 [Gaur 2018]
  49. 49. 58 Really struggling with my [health-related behavior] which is causing [health-related behavior] with a girl. Being a fan of [XXX] community, I am equal to [level of mood] for her. I’m now starting to [drinking] because I can’t cope with the [obsessive compulsive personality disorder] [disturbance in thinking], and [disturbance in thinking]. Is mental health related ? Yes: 0.96 , No: 0.04 Which Mental Health condition? Predicted: Obsessive Compulsive Disorder(True) True: Obsessive Compulsive Disorder DSM-5 Knowledge Graph DSM-5 and Post Correlation Matrix Reasoning over Model: Why model predicted Obsessive Compulsive Disorder ? known Interpretable and Explainable Learning D εRN P εRN W f(W)
  50. 50. 59 K-IL: Semi-Deep Infusion : Matching Reddit Conversation to DSM-5 Domain-specific Knowledge lowers False Alarm Rates. 2005-2016 550K Users 8 Million Conversations 15 Mental Health Subreddits [Gkotsis 2017] [Saravia 2016] [Park 2018] Performance Gains in the outcomes [CIKM 2018]
  51. 51. Other Works: Not Covered 60 Knowledge-aware assessment of severity of suicide risk for early intervention. In The Web Conference 2019 1. Estimating the severity of suicide risk of an individual without the use of Clinician- authorized Questionnaire would hurt the explainability of the model. 2. Challenge in Social Media: (a) Dynamic user Roles and Behavior, (b) Sparsity in Clinician-specific information, and (c) How to adapt Questionnaire to Social Media 3. The study addresses these questions in following ways: a. Clinical knowledge to study user roles (supportive/non-supportive) and behaviors (transitioning between communities on Reddit) b. Columbia-Suicide Severity Rating Scale was modified and adapted to social Media -- Two new classes were added {Supportive, Suicide Indication} c. Contextualization and Abstraction procedures were developed for explainable classification.
  52. 52. Other Works: Not Covered 61 Knowledge-infused Abstractive Summarization of Clinical Diagnostic Interviews In JMIR Mental Health 2021 1. How to pick the relevant question from the recorded script of interview? 2. How to associate the meaningful response to the question being asked? 3. While addressing the 1 and 2, how to incorporate domain knowledge? 4. Addressing 1,2, and 3, how to effectively summarize the long open-ended Clinical Diagnostic Interviews. The strategy for summarizing is heavily grounded in the use of mental health knowledge crafted in prior literature: PHQ-9 Lexicon, DSM-5 Lexicon, Hierarchical knowledge in SNOMED-CT, and Anxiety Lexicon. In natural language processing, a domain-specific language model was designed specific for CDIs and used to identify terms in CDIs which are important for summaries.
  53. 53. Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response Visit Project page: http://wiki.aiisc.ai/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support Amit Sheth, Srinivasan Parthasarathy (OSU), Valerie Shalin (WSU), T.K. Prasad (WSU), Manas Gaur
  54. 54. “ DisasterRecord Demo 63
  55. 55. Discussion http://aiisc.ai

Editor's Notes

  • Slide 3: Inner circle : talks about our research areas and strength
  • Knowledge bias is a bias you can read.

  • Consider combining data-centric/bottom up/statistical learning with knowledge-based/top down techniques

    To improve understanding of simpler content
    To understand complex content and concepts
    To understand heterogeneous/multimodal content
    and a lot more

    ==============

    Move this slide to earlier.
  • The dataset, and the necessary resources for a specific domain (i.e., white supremacy). The datasets and resources for one problem may not work for another.
    V: Consider a graphic at the outset for data driven and knowledge driven approaches, and your infused approach.
    Same for ALONE dataset and online toxicity as well.

    You need to provide a very strong argument as why they are not alone enough to capture context and knowledge.
    Earlier, we have to state that why sole ML solutions and KG solutions have limitations. And why we need knowledge.
    Any evaluation of current state-of-the-art models and their shortcomings, figure or a table.
  • If not solved, big social media platforms make big harms to the society.
    Online harassment example (prolonged): https://www.theguardian.com/society/2018/aug/03/harassed-online-for-13-years-the-victim-who-feels-free-at-last
    Online harassment(teenagers death) - https://www.cnn.com/2018/01/23/us/florida-cyberstalking-charges-girl-suicide/index.html
  • Online harassment subjectivity article : https://www.newscientist.com/article/2140342-online-harassment-on-the-rise-but-no-one-can-agree-what-it-is/
    Sparsity:
    [1] Zeerak Waseem. Are you a racist or am i seeing things? annotator influence on hate speech detection on twitter. In Proc. of the Workshop on NLP and Computational Social Science, pages 138–142. Association for Computational Linguistics, 2016.

    [2] Thoams Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber. Automated hate speech detection and the problem of offensive language. In Proceedings of the 11th Conference on Web and Social Media. AAAI, 2017.

    [3] Zhang, Ziqi & Luo, Lei. (2018). Hate Speech Detection: A Solved Problem? The Challenging Case of Long Tail on Twitter. Semantic Web. Accepted. 10.3233/SW-180338.





  • A Quality Type-aware Annotated Corpus and Lexicon for Harassment Research. Web Science, WebSci 2018, Amsterdam, The Netherlands, May 27-30, 2018





  • https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0227330
    “In general, a single tweet identified as ‘harassing’ may not provoke the same intense negative feeling that we associate with that word in the real-world scenario. However, in practice, ‘conversational’ exchanges containing a sequence of such tweets can rise to the level of harassment causing mental and psychological anguish, and fear of physical harm.”
  • Ambiguity: Different meanings of diagnostic terms

    Sparsity: Low prevalence of relevant content

    Subjectivity: Different perceptions of same concepts.

    Multi Dimensionality: Nature of content with more than one context.

    Multimodal Content: Different modalities of data


    ========================

    Move this slide to earlier and back it up with examples.
  • Challenges:
    Context in social media conversations is fluid and have shades of gray.
    Deeper Understanding of Content through Contextual Dimensions
    False alarms in the models developed and deployed.
    Ambiguity/multi-dimensionality, Sparsity, Subjectivity, Multimodality...
    Bias and lack of transparency : Impacts masses.
    Interpretability + Traceability → Explainability

    Opportunity: The use of knowledge to improve the model
    What is the right knowledge resource to use for quality insights?
  • Knowledge plays an indispensable role in deeper understanding of content
    V: HIGHLIGHT JIHAD in Slide
    With the focus on the role knowledge plays, often complementing/enhancing
    ML and NLP techniques, in contextual “understanding” of data to help solve the problem for which the data is potentially relevant.

    This encompasses topics of information extraction and semantic annotation.

  • Challenges:
    Context in social media conversations is fluid and have shades of gray.
    Deeper Understanding of Content through Contextual Dimensions
    False alarms in the models developed and deployed.
    Ambiguity/multi-dimensionality, Sparsity, Subjectivity, Multimodality...
    Bias and lack of transparency : Impacts masses.
    Interpretability + Traceability → Explainability

    Opportunity: The use of knowledge to improve the model
    What is the right knowledge resource to use for quality insights?
  • 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.
  • To design effective and responsive prevention and policies for public health professionals
    Epidemiological surveillance by providing timely data regarding emerging substances and product form
  • Pairwise correlation between opioid and mental health, suicide.

  • 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.
  • 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.
  • Disambiguating and Contextualizing the tweets using medical knowledge graphs, we observed patterns of improvement in conditions as the decline in the number of tweets on Depression, Addiction, and anxiety

    Much of these is due to meditation, yoga, indoor games, increase use of streaming video platforms
  • Among many external factors, financial events and the specific government interventions have substantial effect in the social quality of people. Specifically, business and individual relief announcements, business closures, increase in unemployment, and stay at home orders. Whenever the unemployment increase is much more significant than the previous week, the social quality is worse; and whereas whenever the individuals and businesses are given financial reliefs, the social quality is better.
  • Multi-hop
    Two-hop
    Changing the post
  • r/BPD: Borderline PErsonality Disorder
    SBI: Suicide Behavior Ideation
    PND: Personality Disorder
    SAD: Substance use and addictive disorders
    DPR: Depression
    DICD: Dissociative Identity Disorder
    OCD: Obsessive Compulsive Disorder
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