  While the study of language as typed on smartphones offers valuable insights,
existing data collection methods often fall short in providing contextual
information and ensuring user privacy. We present a privacy-respectful approach
- context-enriched keyboard logging - that allows for the extraction of
contextual information on the user's input motive, which is meaningful for
linguistics, psychology, and behavioral sciences. In particular, with our
approach, we enable distinguishing language contents by their channel (i.e.,
comments, messaging, search inputs). Filtering by channel allows for better
pre-selection of data, which is in the interest of researchers and improves
users' privacy. We demonstrate our approach on a large-scale six-month user
study (N=624) of language use in smartphone interactions in the wild. Finally,
we highlight the implications for research on language use in human-computer
interaction and interdisciplinary contexts.
