  While transformers have pioneered attention-driven architectures as a
cornerstone of language modeling, their dependence on explicitly contextual
information underscores limitations in their abilities to tacitly learn
overarching textual themes. This study challenges the heuristic paradigm of
performance benchmarking by investigating social media data as a source of
distributed patterns. In stark contrast to networks that rely on capturing
complex long-term dependencies, models of online data inherently lack structure
and are forced to detect latent structures in the aggregate. To properly
represent these abstract relationships, this research dissects empirical social
media corpora into their elemental components, analyzing over two billion
tweets across population-dense locations. We create Bag-of-Word embedding
specific to each city and compare their respective representations. This finds
that even amidst noisy data, geographic location has a considerable influence
on online communication, and that hidden insights can be uncovered without the
crutch of advanced algorithms. This evidence presents valuable geospatial
implications in social science and challenges the notion that intricate models
are prerequisites for pattern recognition in natural language. This aligns with
the evolving landscape that questions the embrace of absolute interpretability
over abstract understanding and bridges the divide between sophisticated
frameworks and intangible relationships.
