For mental health practitioners, relying solely on a patient’s self-reported anxiety assessment can be problematic due to potential biases and inaccuracies influenced by the patient's perceptions and interpretation of symptoms. Individuals may underreport or deny symptoms due to stigma or fear of judgment, and self-reported measures may not capture the full spectrum of anxiety experiences or account for subtle nuances that could impact diagnosis and treatment.
Research into language-based assessment of anxiety disorders as an alternative to traditional self-report tools has uncovered specific language markers predictive of anxiety. In a recent study, participants wrote short stories in response to images from the Thematic Apperception Test (TAT) while also completing anxiety assessments. Their language was analyzed with LIWC and the results were correlated with GAD-7. Findings identified several language categories that are predictive markers of anxiety disorders.
The results showed that respondents high on anxiety tend to use more words that LIWC associates with the expression of negative emotions, fewer words expressing positive emotions, a higher frequency of words that implied semantic differentiation (i.e., but, else) and a lower frequency of words indicating leisure.
Language-based anxiety assessment creates opportunities for more nuanced and accurate evaluations of anxiety disorders, potentially revolutionizing how we understand and address them. Read the complete research paper here.
If you'd like to integrate LIWC-based anxiety detection into your product or solution, the Receptiviti API makes easy. Contact us to learn more.
