@misc{johannaholzlMy22mGirlfriend2026,
  title = {``{{My}} (22m) {{Girlfriend}} (23f) {{Comes Home}} and {{Does Nothing}}'' - {{Gendered Perceptions}} of {{Paid}} and {{Unpaid Work}} in {{Reddit Relationship Discussions}} over {{Time}}},
  author = {Zeyer Gliozzo, Birgit and H{\"o}lzl, Johanna and Zoch, Gundula and Doebler, Philipp},
  year = 2026,
  month = jul,
  publisher = {OSF},
  doi = {10.17605/OSF.IO/F6D7E},
  urldate = {2026-07-16},
  abstract = {The COVID-19 pandemic reignited longstanding debates around gender inequalities in paid and unpaid work. While survey research has advanced our understanding of these disparities, it typically relies on predefined categories and is susceptible to social desirability and recall bias. Online postings, by contrast, capture intimate experiences in real time but rarely include demographic attributes. We leverage a unique dataset of discussions in Reddit relationship communities that combines rich descriptions of conflicts around (un)paid work with demographic information to examine: Which topics do men and women discuss in relationship conflicts around paid and unpaid work? We use Large Language Models (LLMs) and systematically vary GPT-family models and prompting strategies to classify manifest (demographic information) and latent variables (whether posts discuss romantic relationships, paid, and unpaid work) in Reddit posts. Using classifications from the best-performing approach, we apply Structural Topic Models to explore which topics partners discuss and how discussions evolve over time. We find temporary pandemic shifts in topics, and women more often discuss mental health in paid work-related conflicts, while men more frequently emphasize career objectives. In conflicts related to unpaid work, men more frequently express concerns about sexual intimacy. Our analysis offers new insights into topics driving relationship conflicts around (un)paid work. We also contribute to the growing literature on LLMs' capabilities and limitations in classifying social-science constructs. By combining demographic information with sensitive narratives, our dataset captures forms of relational conflict rarely accessible in survey or social-media research, opening promising avenues for future research.},
  archiveprefix = {OSF}
}