Facebook与SMS短信文本消息上的不同可供性并未阻碍基于语言的预测模型的泛化
Different Affordances on Facebook and SMS Text Messaging Do Not Impede Generalization of Language-Based Predictive Models
AI总结：
本研究发现，尽管Facebook和SMS在可供性与心理语言学上存在差异，但基于Facebook的预训练语言模型在SMS上仍能保持较好的跨平台泛化能力，支持即时健康干预。
摘要：
自适应的基于移动设备的健康干预通常使用在非移动设备数据（如社交媒体文本）上训练的机器学习模型，这是因为收集大量文本消息（SMS）数据困难且成本高昂。因此，理解这些平台之间的差异和模型泛化能力对于正确部署至关重要。我们以120名同时分享了两类数据的用户为样本，考察了Facebook和文本消息之间的心理语言学差异及其对跨领域模型性能的影响。我们发现，用户使用Facebook分享经历（如休闲），使用SMS用于任务导向和对话目的（如计划确认），反映了可供性的差异。为了考察这些差异的下游影响，我们使用基于Facebook的预训练语言模型来估计Facebook和SMS上的年龄、性别、抑郁、生活满意度和压力。我们发现，在8个模型中有6个模型的估计值与自我报告之间的相关性没有显著差异。这些结果表明，可以使用基于Facebook的预训练语言模型来实现更好的准确性，以支持即时干预。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
