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Related Experiment Videos

Different Affordances on Facebook and SMS Text Messaging Do Not Impede Generalization of Language-Based Predictive

Tingting Liu^1,2, Salvatore Giorgi^1,2, Xiangyu Tao³

¹National Institute on Drug Abuse.

Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media

|June 3, 2026

Summary

Machine learning models trained on Facebook data generalize well to text messages (SMS) for health interventions. This finding is crucial for developing accurate, just-in-time mobile health tools.

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Area of Science:

Computational linguistics
Digital health
Machine learning

Background:

Mobile health interventions often use machine learning models trained on non-mobile data like social media text.
Collecting large text message (SMS) datasets for training is difficult and expensive.
Understanding cross-platform model generalization is vital for effective deployment.

Purpose of the Study:

To investigate psycho-linguistic differences between Facebook and SMS communication.
To assess the impact of these differences on out-of-domain machine learning model performance.
To evaluate the utility of pre-trained Facebook language models for health-related predictions on SMS data.

Main Methods:

Collected parallel Facebook and SMS data from 120 users.
Analyzed psycho-linguistic features distinguishing Facebook (experience sharing) and SMS (task-oriented communication).
Applied pre-trained Facebook language models to estimate user attributes (age, gender, depression, life satisfaction, stress) on both platforms.

Main Results:

Users exhibit distinct communication styles on Facebook (leisure) versus SMS (conversational, task-oriented).
Pre-trained Facebook language models showed no significant performance drop when applied to SMS data for 6 out of 8 prediction tasks.
Correlations between model estimates and self-reported user states were comparable across platforms.

Conclusions:

Facebook-based language models demonstrate robust generalization to SMS data.
These findings support the use of readily available Facebook data for training models in mobile health interventions.
Leveraging pre-trained Facebook models can enhance accuracy in just-in-time health interventions via SMS.