Large Language Models Show Human-like Social Desirability Biases in Survey Responses

no code implementations • 9 May 2024 • Aadesh Salecha, Molly E. Ireland, Shashanka Subrahmanya, João Sedoc, Lyle H. Ungar, Johannes C. Eichstaedt

As Large Language Models (LLMs) become widely used to model and simulate human behavior, understanding their biases becomes critical.

Robust language-based mental health assessments in time and space through social media

no code implementations • 25 Feb 2023 • Siddharth Mangalik, Johannes C. Eichstaedt, Salvatore Giorgi, Jihu Mun, Farhan Ahmed, Gilvir Gill, Adithya V. Ganesan, Shashanka Subrahmanya, Nikita Soni, Sean A. P. Clouston, H. Andrew Schwartz

Compared to physical health, population mental health measurement in the U. S. is very coarse-grained.

Time Series Analysis

World Trade Center responders in their own words: Predicting PTSD symptom trajectories with AI-based language analyses of interviews

no code implementations • 12 Nov 2020 • Youngseo Son, Sean A. P. Clouston, Roman Kotov, Johannes C. Eichstaedt, Evelyn J. Bromet, Benjamin J. Luft, H Andrew Schwartz

This study sought to test the ability of AI-based language assessments to predict PTSD symptom trajectories among responders.

Detecting Emerging Symptoms of COVID-19 using Context-based Twitter Embeddings

1 code implementation • EMNLP (NLP-COVID19) 2020 • Roshan Santosh, H. Andrew Schwartz, Johannes C. Eichstaedt, Lyle H. Ungar, Sharath C. Guntuku

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.