  The present study proposes a novel method of trend detection and
visualization - more specifically, modeling the change in a topic over time.
Where current models used for the identification and visualization of trends
only convey the popularity of a singular word based on stochastic counting of
usage, the approach in the present study illustrates the popularity and
direction that a topic is moving in. The direction in this case is a distinct
subtopic within the selected corpus. Such trends are generated by modeling the
movement of a topic by using k-means clustering and cosine similarity to group
the distances between clusters over time. In a convergent scenario, it can be
inferred that the topics as a whole are meshing (tokens between topics,
becoming interchangeable). On the contrary, a divergent scenario would imply
that each topics' respective tokens would not be found in the same context (the
words are increasingly different to each other). The methodology was tested on
a group of articles from various media houses present in the 20 Newsgroups
dataset.
