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Found 11 papers, 3 papers with code
Towards Understanding the Role of Gender in Deploying Social Media-Based Mental Health Surveillance Models
Spurred by advances in machine learning and natural language processing, developing social media-based mental health surveillance models has received substantial recent attention.
Qualitative Analysis of Depression Models by Demographics
Models for identifying depression using social media text exhibit biases towards different gender and racial/ethnic groups.
Selecting Shots for Demographic Fairness in Few-Shot Learning with Large Language Models
In this work, we explore the effect of shots, which directly affect the performance of models, on the fairness of LLMs as NLP classification systems.
Transferring Fairness using Multi-Task Learning with Limited Demographic Information
Training supervised machine learning systems with a fairness loss can improve prediction fairness across different demographic groups.
Using Open-Ended Stressor Responses to Predict Depressive Symptoms across Demographics
Stressors are related to depression, but this relationship is complex.
Gender and Racial Fairness in Depression Research using Social Media
While previous research has raised concerns about possible biases in models produced from this data, no study has quantified how these biases actually manifest themselves with respect to different demographic groups, such as gender and racial/ethnic groups.
On the State of Social Media Data for Mental Health Research
Data-driven methods for mental health treatment and surveillance have become a major focus in computational science research in the last decade.
Do Models of Mental Health Based on Social Media Data Generalize?
Proxy-based methods for annotating mental health status in social media have grown popular in computational research due to their ability to gather large training samples.
Scalable End-to-end Recurrent Neural Network for Variable star classification
Our method uses minimal data preprocessing, can be updated with a low computational cost for new observations and light curves, and can scale up to massive datasets.
A Novel Approach for Detection and Ranking of Trendy and Emerging Cyber Threat Events in Twitter Streams
We present a new machine learning and text information extraction approach to detection of cyber threat events in Twitter that are novel (previously non-extant) and developing (marked by significance with respect to similarity with a previously detected event).
Deep multi-survey classification of variable stars
In this work, we present a novel Deep Learning model for light curve classification, mainly based on convolutional units.
