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Identifying self-disclosed anxiety on Twitter: A natural language processing approach
Daniel Zarate1, Michelle Ball2, Maria Prokofieva2
1College of Health and Biomedicine, Royal Melbourne Institute of Technology (RMIT), Australia.
Psychiatry Research
|November 13, 2023
Summary
Social media text analysis reveals distinct language patterns in anxiety. Machine learning accurately predicts anxiety diagnosis based on Twitter posts, highlighting negative sentiment and reduced posting frequency.
Area of Science:
- Computational linguistics
- Mental health informatics
- Social media analytics
Background:
- Social media platforms offer valuable data for mental health research.
- Text analysis of social media posts can identify patterns related to psychological conditions.
- This study focuses on understanding the digital footprint of anxiety on Twitter.
Purpose of the Study:
- To explore distinct language patterns on Twitter associated with self-reported anxiety diagnosis.
- To investigate the predictive power of language features in identifying anxiety.
- To profile user behavior and linguistic characteristics of individuals with anxiety.
Main Methods:
- Comparative analysis of 233,000 tweets from 605 users (anxious vs. non-anxious).
- Utilized natural language processing, including Linguistic Inquiry Word Count (LIWC) and sentiment analysis.
- Employed supervised machine learning models and Latent Profile Analysis (LPA).
Main Results:
- Machine learning models achieved high accuracy (up to 81.1%) in predicting anxiety diagnosis.
- Four distinct user profiles were identified: high sentiment, low sentiment, self-immersed, and normative.
- Anxious users exhibited high negative sentiment, low positive sentiment, reduced posting frequency, and longer texts.
Conclusions:
- Distinct linguistic patterns in Twitter posts correlate with self-reported anxiety.
- These patterns enable accurate prediction of anxiety diagnosis.
- Findings support the development of online mental health campaigns and awareness initiatives.
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