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Program 2022

The Workshop on Computational Linguistics and Clinical Psychology

CLPsych 2022 will be held in conjunction with NAACL 2022

July 15th 2022

You are viewing information about a past edition of CLPsych.

Program

All times are in PST.

8:30 AM

Workshop welcome & announcements

(chairs: Ayah Zirikly, Dana Atzil-Slonim, Maria Liakata)

8:45 AM

Keynote session (moderator: April Foreman)

• Shri Narayanan - Human-centered Behavioral Machine Intelligence
• Finale Doshi-Velez - Bringing ML to Humans
• Liz Shriberg - Mental Health Screening Based on Spoken Language

10:15 AM

Coffee break

10:30 AM

Paper session 1 (discussants: Katie Aafjes-van Doorn, Javier Parapar)

Identifying stable speech-language markers of autism in children: Preliminary evidence from a longitudinal telephony-based study.
Sunghye Cho, Riccardo Fusaroli, Maggie Rose Pelella, Kimberly Tena, Azia Knox, Aili Hauptmann, Maxine Covello, Alison Russell, Judith Miller, Alison Hulink, Jennifer Uzokwe, Kevin Walker, James Fiumara, Juhi Pandey, Christopher Chatham, Christopher Cieri, Robert T. Schultz, Mark Liberman and Julia Parish-Morris
Then and Now: Quantifying the Longitudinal Validity of Self-Disclosed Depression Diagnoses.
Keith Harrigian and Mark Dredze
Tracking Mental Health Risks and Coping Strategies in Healthcare Workers' Online Conversations Across the COVID-19 Pandemic.
Molly Ireland, Kaitlin Adams and Sean A. Farrell

11:20 AM

Shared task session (chairs: Adam Tsakalidis & Maria Liakata)

Overview of the CLPsych 2022 shared task: Capturing moments of change in longitudinal user posts.
Adam Tsakalidis, Jenny Chim, Iman Munire Bilal, Ayah Zirikly, Dana Atzil-Slonim, Federico Nanni, Philip Resnik, Manas Gaur, Kaushik Roy, Becky Inkster, Jeff Leintz, and Maria Liakata.
Emotionally-Informed Models for Detecting Moments of Change and Suicide Risk Levels in Longitudinal Social Media Data
Ulya Bayram and Lamia Benhiba
Detecting Moments of Change and Suicidal Risks in Longitudinal User Texts Using Multi-task Learning
Tayyaba Azim, Loitongbam Gyanendro Singh and Stuart E. Middleton

12:00 PM

Lunch break

12:45 PM

Panel discussion (moderator: Paul Middlebrooks)
• Glen Coppersmith

• Munmun De Choudhury
• Mark Dredze
• Zac Imel

2:15 PM

Coffee break

2:30 PM

Poster session (chairs: Molly Ireland)

Main workshop posters

Detecting Suicidality with a Contextual Graph Neural Network
Daeun Lee, Migyeong Kang, Minji Kim and Jinyoung Han
Identifying Distorted Thinking in Patient-Therapist Text Message Exchanges by Leveraging Dynamic Multi-Turn Context
Kevin Lybarger, Justin Tauscher, Xiruo Ding, Dror Ben-Zeev and Trevor Cohen
Masking Morphosyntactic Categories to Evaluate Salience for Schizophrenia Diagnosis
Yaara Shriki, Ido Ziv, Nachum Dershowitz, Eiran Vadim Harel and Kfir Bar
• Nonsuicidal Self-Injury and Substance Use Disorders: A Shared Language of Addiction
Salvatore Giorgi, McKenzie Himelein-Wachowiak, Daniel Habib, Lyle Ungar and Brenda Curtis
Are You Really Okay? A Transfer Learning-based Approach for Identification of Underlying Mental Illnesses
Ankit Aich and Natalie Parde
Learning to Automate Follow-up Question Generation using Process Knowledge for Depression Triage on Reddit Posts
Shrey Gupta, Anmol Agarwal, Manas Gaur, Kaushik Roy, Vignesh Narayanan, Ponnurangam Kumaraguru and Amit Sheth
Comparing emotion feature extraction approaches for predicting depression and anxiety
Hannah A. Burkhardt, Michael D. Pullmann, Thomas D. Hull, Patricia A. Areán and Trevor Cohen
• Measuring Linguistic Synchrony in Psychotherapy
Natalie Shapira, Dana Atzil-Slonim, Rivka Tuval Mashiach and Ori Shapira





Shared task posters

• Approximate Nearest Neighbour Extraction Techniques and Neural Networks for Suicide Risk Prediction in the CLPsych 2022 Shared Task
Hermenegildo Fabregat Marcos, Ander Cejudo, Juan Martinez-Romo, Alicia Perez, Lourdes Araujo, Nuria Lebeña, Maite Oronoz and Arantza Casillas
• Exploring transformers and time lag features for predicting changes in mood over time
John Culnan, Damian Yukio Romero Diaz and Steven Bethard
• Capturing Changes in Mood Over Time in Longitudinal Data Using Ensemble Methodologies
Ana-Maria Bucur, Hyewon Jang and Farhana Ferdousi Liza
• WWBP-SQT-lite: Multi-level Models and Difference Embeddings for Moments of Change Identification in Mental Health Forums
Adithya V Ganesan, Vasudha Varadarajan, Juhi Mittal, Shashanka Subrahmanya, Matthew Matero, Nikita Soni, Sharath Chandra Guntuku, Johannes C. Eichstaedt and H. Andrew Schwartz
• Predicting Moments of Mood Changes Overtime from Imbalanced Social Media Data
Falwah Alhamed, Julia Ive and Lucia Specia
• Towards Capturing Changes in Mood and Identifying Suicidality Risk
Sravani Boinepelli, Shivansh Subramanian, Abhijeeth Reddy Singam, Tathagata Raha and Vasudeva Varma
• Multi-Task Learning to Capture Changes in Mood Over Time
Prasadith Kirinde Gamaarachchige, Ahmed Husseini Orabi, Mahmoud Husseini Orabi and Diana Inkpen
• Emotionally-Informed Models for Detecting Moments of Change and Suicide Risk Levels in Longitudinal Social Media Data
Ulya Bayram and Lamia Benhiba

3:40 PM

Social break (chairs: Dana Atzil-Slonim & Bart Desmet)

4:10 PM

Paper session 2 (discussants: Wendy Ingram, Adam Miner)

• Explaining Models of Mental Health via Clinically Grounded Auxiliary Tasks
Ayah Zirikly and Mark Dredze
• Psychotherapy is Not One Thing: Simultaneous Modeling of Different Therapeutic Approaches
Maitrey Mehta, Derek D. Caperton, Katherine Axford, Lauren Weitzman, David Atkins, Vivek Srikumar and Zac Imel
• The ethical role of computational linguistics in digital psychological formulation and suicide prevention.
Martin P. Orr, Kirsten Van Kessel and Dave Parry
* DEPAC: A Corpus for Depression and Anxiety Detection from Speech
Mashrura Tasnim, Malikeh Ehghaghi, Brian Diep, Jeketerina Novikova

5:10 PM

Closing remarks (OC)

5:20 PM

Social dinner (chair: Andrew Yates)

Speakers and Panelists

Dr. Shrikanth (Shri) Narayanan

Shrikanth (Shri) Narayanan is University Professor and Niki & C. L. Max Nikias Chair in Engineering at the University of Southern California (USC) and is Professor of Electrical and Computer Engineering, Computer Science, Linguistics, Psychology, Neuroscience, Pediatrics and Otolaryngology-Head and Neck Surgery, and Director of the Ming Hsieh Institute. Prior to USC he was with AT&T Bell Labs and AT&T Research. He is an interdisciplinary scientist-engineer-educator whose work aims to create engineering approaches to human-centered sensing/imaging, signal and information processing, and computational modeling focused on human communication, interaction, emotions and behavior. These efforts aim at both scientifically illuminating human speech, language and multimodal mechanisms, and in creating robust, inclusive and equitable technologies for supporting and enhancing human experiences in domains ranging from defense and security to health, learning, and media studies.  He is a Guggenheim Fellow and a Fellow of the Acoustical Society of America, IEEE, ISCA, American Association for the Advancement of Science, Association for Psychological Science, American Institute for Medical and Biological Engineering and the National Academy of Inventors.

Dr. Finale Doshi-Velez

Finale Doshi-Velez is a Gordon McKay Professor in Computer Science at the Harvard Paulson School of Engineering and Applied Sciences. She completed her MSc from the University of Cambridge as a Marshall Scholar, her PhD from MIT, and her postdoc at Harvard Medical School. Her interests lie at the intersection of machine learning, healthcare, and interpretability.

Dr. Elizabeth Shriberg

Elizabeth Shriberg is currently Chief Science Officer at Ellipsis Health, a San Francisco start-up using spoken language for mental health screening and monitoring.  She is also an Affiliate at Johns Hopkins U. Dr. Shriberg was previously a Senior Principal Scientist at Amazon and a Principal Researcher at Microsoft. She spent two decades as a Principal Scientist at SRI International, and was an External Fellow at ICSI in Berkeley.  Her interests include health applications, affective computing, computational prosody, dialog and discourse, speaker recognition, and modeling of speech disfluencies. She has over 300 publications and patents, and has served on a wide range of committees and boards in the speech technology community.  She also advises start-ups in conversational AI.

Dr. April C. Foreman

April C. Foreman, Ph.D., is a Licensed Psychologist serving Veterans as the Program Director for Technology and Innovation for the Veterans Crisis Line. She served as an Executive Committee member for the Board of the American Association of Suicidology, and as the 2017 Acting Director of Technology and Innovation for the VA’s Office of Suicide Prevention. She is a member of the team that launched OurDataHelps.org, a recognized innovation in data donation for ground breaking suicide research. She is passionate about helping people with severe (sometimes lethal) emotional pain, and in particular advocates for people with Borderline Personality Disorder, which has one of the highest mortality rates of all mental illnesses. She is known for her work at the intersection of technology, social media, and mental health, with nationally recognized implementations of innovations in the use of technology and mood tracking. She is the 2015 recipient of the Roger J. Tierney Award for her work as a founder and moderator of the first sponsored regular mental health chat on Twitter, the weekly Suicide Prevention Social Media chat (#SPSM, sponsored by the American Association of Suicidology, AAS). Her dream is to use her unique skills and vision to build a mental health system effectively and elegantly designed to serve the people who need it.

Dr. Glen Coppersmith

Glen is the Chief Data Officer at SonderMind after their acquistion of Qntfy, for which he was the Founder and CEO. He is recognized as a leader and pioneer in the space of mental health and data science, with early and frequent peer-reviewed publications on the advancements made at SonderMind and Qntfy. Glen’s work has been covered in several major publications including the Today Show, Crunchbase, Mashable, The Mighty and Scientific American. He frequently speaks about the scientific advances, ethics, and pragmatics of using data to spark sustained improvements in wellbeing, including The White House, National Institute of Mental Health, SXSW, and NASA.

Dr. Munmun De Choudhury

Munmun De Choudhury is an Associate Professor of Interactive Computing at Georgia Tech. Dr. De Choudhury is best known for laying the foundation of a new line of research that develops computational techniques towards understanding and improving mental health outcomes, through ethical analysis of social media data. To do this work, she adopts a highly interdisciplinary approach, combining social computing, machine learning, and natural language analysis with insights and theories from the social, behavioral, and health sciences. Dr. De Choudhury has been recognized with the 2021 ACM-W Rising Star Award, 2019 Complex Systems Society – Junior Scientific Award, numerous best paper and honorable mention awards from the ACM and AAAI, and features and coverage in popular press like the New York Times, the NPR, and the BBC. Dr. De Choudhury currently serves on the Board of Directors of the International Society for Computational Social Science and on the Steering Committee of the International Conference on Web and Social Media, the leading conference on interdisciplinary studies of social media. Earlier, Dr. De Choudhury was a faculty associate with the Berkman Klein Center for Internet and Society at Harvard, a postdoc at Microsoft Research, and obtained her PhD in Computer Science from Arizona State University.

Dr. Mark Dredze

Mark Dredze is the John C Malone Associate Professor of Computer Science at Johns Hopkins University. He is affiliated with the Malone Center for Engineering in Healthcare, the Center for Language and Speech Processing, among others. He holds a secondary appointment in the Biomedical Informatics & Data Science Section (BIDS), under the Department of Medicine (DOM), Division of General Internal Medicine (GIM) in the School of Medicine. He obtained his PhD from the University of Pennsylvania in 2009. Prof. Dredze’s research develops statistical models of language with applications to social media analysis, public health and clinical informatics. Within Natural Language Processing he focuses on statistical methods for information extraction but has considered a wide range of NLP tasks, including syntax, semantics, sentiment and spoken language processing. His work in public health includes tobacco control, vaccination, infectious disease surveillance, mental health, drug use, and gun violence prevention. He also develops new methods for clinical NLP on medical records.

Dr. Zac Imel

Zac Imel, Ph.D. is co-founder and Chief Science Officer at Lyssn.io. He is a psychologist by training and is a Professor and Director of Clinical Training for the Counseling Psychology PhD program at the University of Utah. He is the author of over 100 scientific publications, and PI or Co-I of projects funded by the US National Institutes on Drug Abuse, Mental Health, and Alcohol Abuse and Alcoholism, as well as the National Science Foundation, and the Patient-Centered Outcomes Research Institute. In 2014, he received the Early Career Award from Society for the Advancement of Psychotherapy of The American Psychological Association. He is also co-author of the 2nd edition of the book, “The Great Psychotherapy Debate”. The book reviews findings from a vast array of psychotherapy studies and outlines a scientific understanding of how humans heal in a social context.  Along with colleagues in speech signal processing and computer science, he co-led the first interdisciplinary work applying statistical NLP to psychotherapy conversations.

Dr. Paul Middlebrooks

Paul Middlebrooks produces and hosts the Brain Inspired podcast, which explores the intersection of artificial intelligence and neuroscience to help understand and build intelligence. In addition, he created an online course, Neuro-AI: The Quest to Explain Intelligence, which explores these topics systematically. He spent ten years as a computational neuroscientist at the University of Pittsburgh and Vanderbilt University, studying decision-making in non-human primates, using neurophysiological recordings, behavioral tasks and analysis, and mathematical psychology models. 

Discussants

Dr. Katie Aafjes-van Doorn

Dr. Katie Aafjes-van Doorn is Assistant Professor of Clinical Psychology at the Clinical Psychology Program of the Ferkauf Graduate School of Psychology. She received a MSc in Clinical Psychology from the Vrije Universiteit in Amsterdam, as well as a MSc in Psychological Research and a doctorate in Clinical Psychology from University of Oxford. Over the years, she has worked clinically in different settings within the National Health Service, in the UK as well as at a psychoanalytic community clinic in San Francisco. Most recently, Dr. Aafjes-van Doorn completed a one-year postdoctoral research fellowship at the Gordon F. Derner School of Psychology, Adelphi University.

Dr. Javier Parapar

Dr. Javier Parapar is an Associate Professor at the Information Retrieval Lab and the University of A Coruña. His PhD Thesis was about new models and estimation of Relevance Models in Information Retrieval. While completing his PhD, he visited Yahoo! Research and the University of Lugano. During his postdoc phase, his research was centred on Recommender Systems, Information Retrieval evaluation, and Text Mining. In 2020 he was Visiting Faculty Researcher in Google AI. He served the research community in different roles. He was president of the Spanish Information Retrieval Society (2014-2018). Since 2018, he has been part of the editorial board of Information Processing & Management (Q1 JCR). He regularly serves as PC member/reviewer of the top conferences and journals in his area. He, with David Losada and Fabio Crestani, also has been organizing the eRisk international competition (https://www.erisk.org) on the early risk prediction on the internet since 2017. Under this initiative, they have created datasets and evaluation methodologies used by hundreds of researchers worldwide.

Dr. Wendy Ingram

Wendy Ingram is the Co-Founder and CEO of Dragonfly Mental Health, a nonprofit dedicated to cultivating excellent mental health among academics worldwide.  She is also a research scientist at Geisinger Health working on biomedical informatics projects aimed at improving healthcare outcomes following surgery and better understanding patient response to electroconvulsive therapy (ECT). She serves as the Chair of the American Medical Informatics Association Mental Health Informatics Working Group, and enjoys consulting for biomedical technology companies that focus on using technology to advance mental health care.  

She is dedicated to (1) understanding the underlying biology of mental illness through research, and (2) dismantling the stigma against these illnesses through advocacy, education, and systemic change.

Dr. Adam Miner

Adam Miner is a Clinical Assistant Professor in Stanford’s Department of Psychiatry and Behavioral Sciences. He is a researcher and licensed clinical psychologist who provides adult outpatient mental health treatment. His research intersects clinical psychology, epidemiology, and clinical informatics, using experimental and observational studies to improve the ability of conversational artificial intelligence to recognize and respond to sensitive mental health issues.

Dr. Miner’s work has been published in JAMA journals, npj Digital Medicine, Empirical Methods in Natural Language Processing (EMNLP), and highlighted in popular media outlets such as the New York Times, NPR, and the Wall Street Journal.

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