Article

Machine learning of language use on Twitter reveals weak and non-specific predictions

2021-03-23

Abstract excerpt

<p>Background: Depressed individuals use language differently than healthy controls and it has been proposed that social media posts could therefore be used to identify depression. But much of the evidence behind this claim relies on indirect measures of mental health that are sometimes circular, such as statements of self-diagnosis (“Got an OCD diagnosis today”) on social media or membership in disorder-specific...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 7b27b331-2acc-57d5-b9d6-5f46990c96df
DOI: 10.31234/osf.io/rzx73

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Machine learning of language use on Twitter reveals weak and non-specific predictionsDOI 10.31234/osf.io/rzx73Read

Digital conversations about depression among Hispanics and non-Hispanics in the US: A big‐data, machine learning analysisSemantic proximity Read

iSense: AI-Based Model and App to Identify Linguistic Biomarkers of Mood DisordersSemantic proximity Read

Studies of Online Cohorts for Internalizing symptoms and Language (SOCIAL) I and II: Baseline assessments and rationaleSemantic proximity Read

Using a Fine-tuned Large Language Model for Symptom-based Depression EvaluationSemantic proximity Read

Predicting depression using electronic health records data: A systematic reviewSemantic proximity Read

A Hybrid Deep Learning Model to Predict the Impact of COVID-19 on Mental Health form Social Media Big DataSemantic proximity Read

Generalizability of Clinical Prediction Models in Mental Health - Real-World Validation of Machine Learning Models for Depressive Symptom PredictionSemantic proximity Read

Depression diagnosis using Artificial Intelligence: a systematic reviewSemantic proximity Read

Deep Neural Networks Detect Suicide Risk from Textual Facebook PostsSemantic proximity Read

Semantic signals in self-reference: The detection and prediction of depressive symptoms from the daily diary entries of a sample with major depressive disorderSemantic proximity Read

Inter-rater Reliability of an LLM in Predicting Depression Among Indian AdultsSemantic proximity Read

Leveraging Large Language Models for Digital Phenotyping: Detecting Depressive State Changes for Patients with Depressive EpisodesSemantic proximity Read