  Adaptive mobile device-based health interventions often use machine learning
models trained on non-mobile device data, such as social media text, due to the
difficulty and high expense of collecting large text message (SMS) data.
Therefore, understanding the differences and generalization of models between
these platforms is crucial for proper deployment. We examined the
psycho-linguistic differences between Facebook and text messages, and their
impact on out-of-domain model performance, using a sample of 120 users who
shared both. We found that users use Facebook for sharing experiences (e.g.,
leisure) and SMS for task-oriented and conversational purposes (e.g., plan
confirmations), reflecting the differences in the affordances. To examine the
downstream effects of these differences, we used pre-trained Facebook-based
language models to estimate age, gender, depression, life satisfaction, and
stress on both Facebook and SMS. We found no significant differences in
correlations between the estimates and self-reports across 6 of 8 models. These
results suggest using pre-trained Facebook language models to achieve better
accuracy with just-in-time interventions.
