Search Results for author:
Found 13 papers, 3 papers with code
Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Support
Advances in artificial intelligence (AI) are enabling systems that augment and collaborate with humans to perform simple, mechanistic tasks like scheduling meetings and grammar-checking text.
Transformer-Based Behavioral Representation Learning Enables Transfer Learning for Mobile Sensing in Small Datasets
This architecture combines benefits from CNN and Trans-former architectures to (1) enable better prediction performance by learning directly from raw minute-level sensor data without the need for handcrafted features by up to 0. 33 ROC AUC, and (2) use pretraining to outperform simpler neural models and boosted decision trees with data from as few a dozen participants.
Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach
Learning such transformations is challenging and requires a deep understanding of empathy while maintaining conversation quality through text fluency and specificity to the conversational context.
Adjusting for Confounders with Text: Challenges and an Empirical Evaluation Framework for Causal Inference
Across 648 experiments and two datasets, we evaluate every commonly used causal inference method and identify their strengths and weaknesses to inform social media researchers seeking to use such methods, and guide future improvements.
A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support
We develop a novel unifying theoretically-grounded framework for characterizing the communication of empathy in text-based conversations.
CORAL: COde RepresentAtion Learning with Weakly-Supervised Transformers for Analyzing Data Analysis
Large scale analysis of source code, and in particular scientific source code, holds the promise of better understanding the data science process, identifying analytical best practices, and providing insights to the builders of scientific toolkits.
Population-Scale Study of Human Needs During the COVID-19 Pandemic: Analysis and Implications
Most work to date on mitigating the COVID-19 pandemic is focused urgently on biomedicine and epidemiology.
Boba: Authoring and Visualizing Multiverse Analyses
Multiverse analysis is an approach to data analysis in which all "reasonable" analytic decisions are evaluated in parallel and interpreted collectively, in order to foster robustness and transparency.
Human-Computer Interaction
The Effect of Moderation on Online Mental Health Conversations
We found that participation in group mental health discussions led to improvements in psychological perspective, and that these improvements were larger in moderated conversations.
Learning Individualized Cardiovascular Responses from Large-scale Wearable Sensors Data
We consider the problem of modeling cardiovascular responses to physical activity and sleep changes captured by wearable sensors in free living conditions.
Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health
Mental illness is one of the most pressing public health issues of our time.
Detection Bank: An Object Detection Based Video Representation for Multimedia Event Recognition
While low-level image features have proven to be effective representations for visual recognition tasks such as object recognition and scene classification, they are inadequate to capture complex semantic meaning required to solve high-level visual tasks such as multimedia event detection and recognition.
How to Ask for a Favor: A Case Study on the Success of Altruistic Requests
We present a case study of altruistic requests in an online community where all requests ask for the very same contribution and do not offer anything tangible in return, allowing us to disentangle what is requested from textual and social factors.
