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Related Experiment Video

Updated: Jul 2, 2025

A New Method for Inducing a Depression-Like Behavior in Rats

A New Method for Inducing a Depression-Like Behavior in Rats

Published on: February 22, 2018

Negative affect variability differs between anxiety and depression on social media.

Lauren A Rutter^1,2, Marijn Ten Thij^1,3,4, Lorenzo Lorenzo-Luaces^1,2

¹Center for Social and Biomedical Complexity, Indiana University Bloomington, Bloomington, IN, United States of America.

|February 21, 2024

Summary

Individuals with depression exhibit greater negative affect variability than those with anxiety disorders. This study demonstrates social media sentiment analysis can effectively measure mental health at scale.

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Area of Science:

Psychiatry
Computational Social Science
Psychology

Background:

Negative affect variability is linked to internalizing psychopathology, including depression and anxiety.
The Contrast Avoidance Model (CAM) posits that anxiety involves avoiding negative emotional shifts through worry.
Recent research suggests CAM's applicability to major depression and social phobia due to their characteristic negative affect changes.

Purpose of the Study:

To compare negative affect variability between individuals diagnosed with anxiety disorders and depressive disorders.
To investigate the utility of online communication sentiment analysis for assessing mental health conditions.

Main Methods:

Analyzed Twitter communications from 1,853 individuals diagnosed with anxiety (n=896) or depression (n=957).
Calculated mean negative affect (NA) and NA variability using the Valence Aware Dictionary for Sentiment Reasoning (VADER).

Main Results:

Individuals with depression diagnoses (D cohort) showed significantly higher NA variability than those with anxiety diagnoses (A cohort).
The D cohort also exhibited higher overall average NA compared to the A cohort.
Statistical significance was confirmed with U-statistics, p-values, and effect sizes (r, d).

Conclusions:

Depression diagnoses are associated with greater negative affect variability than anxiety disorders.
Sentiment analysis of large-scale social media data offers a viable method for studying mental health effects.
This approach enables the scalable study of naturally occurring mental health phenomena.