Huy Vu


2020

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Predicting Responses to Psychological Questionnaires from Participants’ Social Media Posts and Question Text Embeddings
Huy Vu | Suhaib Abdurahman | Sudeep Bhatia | Lyle Ungar
Findings of the Association for Computational Linguistics: EMNLP 2020

Psychologists routinely assess people’s emotions and traits, such as their personality, by collecting their responses to survey questionnaires. Such assessments can be costly in terms of both time and money, and often lack generalizability, as existing data cannot be used to predict responses for new survey questions or participants. In this study, we propose a method for predicting a participant’s questionnaire response using their social media texts and the text of the survey question they are asked. Specifically, we use Natural Language Processing (NLP) tools such as BERT embeddings to represent both participants (via the text they write) and survey questions as embeddings vectors, allowing us to predict responses for out-of-sample participants and questions. Our novel approach can be used by researchers to integrate new participants or new questions into psychological studies without the constraint of costly data collection, facilitating novel practical applications and furthering the development of psychological theory. Finally, as a side contribution, the success of our model also suggests a new approach to study survey questions using NLP tools such as text embeddings rather than response data used in traditional methods.

2019

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Suicide Risk Assessment with Multi-level Dual-Context Language and BERT
Matthew Matero | Akash Idnani | Youngseo Son | Salvatore Giorgi | Huy Vu | Mohammad Zamani | Parth Limbachiya | Sharath Chandra Guntuku | H. Andrew Schwartz
Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology

Mental health predictive systems typically model language as if from a single context (e.g. Twitter posts, status updates, or forum posts) and often limited to a single level of analysis (e.g. either the message-level or user-level). Here, we bring these pieces together to explore the use of open-vocabulary (BERT embeddings, topics) and theoretical features (emotional expression lexica, personality) for the task of suicide risk assessment on support forums (the CLPsych-2019 Shared Task). We used dual context based approaches (modeling content from suicide forums separate from other content), built over both traditional ML models as well as a novel dual RNN architecture with user-factor adaptation. We find that while affect from the suicide context distinguishes with no-risk from those with “any-risk”, personality factors from the non-suicide contexts provide distinction of the levels of risk: low, medium, and high risk. Within the shared task, our dual-context approach (listed as SBU-HLAB in the official results) achieved state-of-the-art performance predicting suicide risk using a combination of suicide-context and non-suicide posts (Task B), achieving an F1 score of 0.50 over hidden test set labels.