Search Results for author:
Found 49 papers, 10 papers with code
WWBP-SQT-lite: Multi-level Models and Difference Embeddings for Moments of Change Identification in Mental Health Forums
Psychological states unfold dynamically; to understand and measure mental health at scale we need to detect and measure these changes from sequences of online posts.
Understanding Weekly COVID-19 Concerns through Dynamic Content-Specific LDA Topic Modeling
The novelty and global scale of the COVID-19 pandemic has lead to rapid societal changes in a short span of time.
SOCIALITE-LLAMA: An Instruction-Tuned Model for Social Scientific Tasks
Social science NLP tasks, such as emotion or humor detection, are required to capture the semantics along with the implicit pragmatics from text, often with limited amounts of training data.
Comparing Pre-trained Human Language Models: Is it Better with Human Context as Groups, Individual Traits, or Both?
We compare pre-training models with human context via 1) group attributes, 2) individual users, and 3) a combined approach on 5 user- and document-level tasks.
Adaptive Language-based Mental Health Assessment with Item-Response Theory
Mental health issues widely vary across individuals - the manifestations of signs and symptoms can be fairly heterogeneous.
Large Human Language Models: A Need and the Challenges
As research in human-centered NLP advances, there is a growing recognition of the importance of incorporating human and social factors into NLP models.
Systematic Evaluation of GPT-3 for Zero-Shot Personality Estimation
Very large language models (LLMs) perform extremely well on a spectrum of NLP tasks in a zero-shot setting.
Psychological Metrics for Dialog System Evaluation
We present metrics for evaluating dialog systems through a psychologically-grounded "human" lens in which conversational agents express a diversity of both states (e. g., emotion) and traits (e. g., personality), just as people do.
Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge
While transformer-based systems have enabled greater accuracies with fewer training examples, data acquisition obstacles still persist for rare-class tasks -- when the class label is very infrequent (e. g. < 5% of samples).
Active Learning
Implicit Discourse Relation Classification
+1
Robust language-based mental health assessments in time and space through social media
Compared to physical health, population mental health measurement in the U. S. is very coarse-grained.
Human Language Modeling
Natural language is generated by people, yet traditional language modeling views words or documents as if generated independently.
Evaluating Contextual Embeddings and their Extraction Layers for Depression Assessment
Recent works have demonstrated ability to assess aspects of mental health from personal discourse.
MeLT: Message-Level Transformer with Masked Document Representations as Pre-Training for Stance Detection
Much of natural language processing is focused on leveraging large capacity language models, typically trained over single messages with a task of predicting one or more tokens.
On the Distribution, Sparsity, and Inference-time Quantization of Attention Values in Transformers
This informs the design of an inference-time quantization technique using both pruning and log-scaled mapping which produces only a few (e. g. $2^3$) unique values.
Empirical Evaluation of Pre-trained Transformers for Human-Level NLP: The Role of Sample Size and Dimensionality
In human-level NLP tasks, such as predicting mental health, personality, or demographics, the number of observations is often smaller than the standard 768+ hidden state sizes of each layer within modern transformer-based language models, limiting the ability to effectively leverage transformers.
Autoregressive Affective Language Forecasting: A Self-Supervised Task
Human natural language is mentioned at a specific point in time while human emotions change over time.
Detecting Emerging Symptoms of COVID-19 using Context-based Twitter Embeddings
In this paper, we present an iterative graph-based approach for the detection of symptoms of COVID-19, the pathology of which seems to be evolving.
Hierarchical Modeling for User Personality Prediction: The Role of Message-Level Attention
Not all documents are equally important.
Quantifying Community Characteristics of Maternal Mortality Using Social Media
While most mortality rates have decreased in the US, maternal mortality has increased and is among the highest of any OECD nation.
Correcting Sociodemographic Selection Biases for Population Prediction from Social Media
However, social media users are not typically a representative sample of the intended population -- a "selection bias".
Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview
In this paper, we propose a unifying conceptualization: the predictive bias framework for NLP.
Suicide Risk Assessment with Multi-level Dual-Context Language and BERT
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).
Tweet Classification without the Tweet: An Empirical Examination of User versus Document Attributes
NLP naturally puts a primary focus on leveraging document language, occasionally considering user attributes as supplemental.
Learning Emotion from 100 Observations: Unexpected Robustness of Deep Learning under Strong Data Limitations
One of the major downsides of Deep Learning is its supposed need for vast amounts of training data.
Identifying Locus of Control in Social Media Language
Individuals express their locus of control, or {``}control{''}, in their language when they identify whether or not they are in control of their circumstances.
Causal Explanation Analysis on Social Media
Understanding causal explanations - reasons given for happenings in one's life - has been found to be an important psychological factor linked to physical and mental health.
Residualized Factor Adaptation for Community Social Media Prediction Tasks
Predictive models over social media language have shown promise in capturing community outcomes, but approaches thus far largely neglect the socio-demographic context (e. g. age, education rates, race) of the community from which the language originates.
Predicting Human Trustfulness from Facebook Language
Trustfulness -- one's general tendency to have confidence in unknown people or situations -- predicts many important real-world outcomes such as mental health and likelihood to cooperate with others such as clinicians.
Using Search Queries to Understand Health Information Needs in Africa
The lack of comprehensive, high-quality health data in developing nations creates a roadblock for combating the impacts of disease.
CLPsych 2018 Shared Task: Predicting Current and Future Psychological Health from Childhood Essays
We describe the shared task for the CLPsych 2018 workshop, which focused on predicting current and future psychological health from an essay authored in childhood.
Domain Adaptation from User-level Facebook Models to County-level Twitter Predictions
Several studies have demonstrated how language models of user attributes, such as personality, can be built by using the Facebook language of social media users in conjunction with their responses to psychology questionnaires.
Human Centered NLP with User-Factor Adaptation
We pose the general task of user-factor adaptation {--} adapting supervised learning models to real-valued user factors inferred from a background of their language, reflecting the idea that a piece of text should be understood within the context of the user that wrote it.
Assessing Objective Recommendation Quality through Political Forecasting
Recommendations are often rated for their subjective quality, but few researchers have studied comment quality in terms of objective utility.
DLATK: Differential Language Analysis ToolKit
We present Differential Language Analysis Toolkit (DLATK), an open-source python package and command-line tool developed for conducting social-scientific language analyses.
On the Distribution of Lexical Features at Multiple Levels of Analysis
Natural language processing has increasingly moved from modeling documents and words toward studying the people behind the language.
Latent Human Traits in the Language of Social Media: An Open-Vocabulary Approach
Taking advantage of linguistic information available through Facebook, we study the process of inferring a new set of potential human traits based on unprompted language use.
Using Twitter Language to Predict the Real Estate Market
We explore whether social media can provide a window into community real estate -foreclosure rates and price changes- beyond that of traditional economic and demographic variables.
