Many individuals affected by Social Anxiety Disorder turn to social media platforms to share their experiences and seek advice. This in- cludes discussing the potential benefits of en- gaging with outdoor environments. As part of #SMM4H 2024, Shared Task 3 focuses on classifying the effects of outdoor spaces on social anxiety symptoms in Reddit posts. In our contribution to the task, we explore the ef- fectiveness of domain-specific models (trained on social media data – SocBERT) against general domain models (trained on diverse datasets – BERT, RoBERTa, GPT-3.5) in pre- dicting the sentiment related to outdoor spaces. Further, we assess the benefits of augment- ing sparse human-labeled data with synthetic training instances and evaluate the complemen- tary strengths of domain-specific and general classifiers using an ensemble model. Our re- sults show that (1) fine-tuning small, domain- specific models generally outperforms large general language models in most cases. Only one large language model (GPT-4) exhibits per- formance comparable to the fine-tuned mod- els (52% F1). Further, we find that (2) syn- thetic data does improve the performance of fine-tuned models in some cases, and (3) mod- els do not appear to complement each other in our ensemble setup.