Machine Learning
Automated computer-based feedback in expressive writing
Computers in Human Behavior
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A strength of computer-based interventions is the capacity to tailor to individual differences, but most studies have tailored to self-report, rather than linguistic, data. The purpose of the present study was to develop and evaluate the effects of linguistically-tailored feedback on an Internet-based expressive writing intervention. Two hundred eighty-one participants were asked to engage in 3days of expressive writing and were randomly assigned to one of 3 feedback conditions: control (no feedback), simple (feedback about levels of emotional expression), and directive (simple feedback+suggestions for emotional processing). A Perl-based implementation of Linguistic Inquiry and Word Count (LIWC) was developed in order to provide dynamic feedback to participants based on levels of emotional expression identified in their writing. This implementation provided near-perfect correlations with standard LIWC output, r's=.98-1.00. Positive and total, but not negative, emotional expression increased over time for those who received simple or directive feedback. These findings suggest that linguistically-tailored feedback has the potential to alter patterns of engagement in computer-based interventions. However, additional research is needed to identify the most effective types of feedback in order to enhance immediate effects on writing and longitudinal effects on relevant outcomes.