  While transformers have pioneered attention-driven architectures as a
cornerstone of research, their dependence on explicitly contextual information
underscores limitations in their abilities to tacitly learn overarching textual
themes. This study investigates social media data as a source of distributed
patterns, challenging the heuristic paradigm of performance benchmarking. In
stark contrast to networks that rely on capturing complex long-term
dependencies, models of online data inherently lack structure and are forced to
learn underlying patterns in the aggregate. To properly represent these
abstract relationships, this research dissects empirical social media corpora
into their elemental components and analyzes over two billion tweets across
population-dense locations. Exploring the relationship between location and
vernacular in Twitter data, we employ Bag-of-Words models specific to each city
and evaluate their respective representation. This demonstrates that hidden
insights can be uncovered without the crutch of advanced algorithms and
demonstrates that even amidst noisy data, geographic location has a
considerable influence on online communication. This evidence presents tangible
insights regarding geospatial communication patterns and their implications in
social science. It also challenges the notion that intricate models are
prerequisites for pattern recognition in natural language, aligning with the
evolving landscape that questions the embrace of absolute interpretability over
abstract understanding. This study bridges the divide between sophisticated
frameworks and intangible relationships, paving the way for systems that blend
structured models with conjectural reasoning.

Computation and Language;Social and Information Networks