  Online social connections occur within a specific conversational context.
Prior work in network analysis of social media data attempts to contextualize
data through filtering. We propose a method of contextualizing online
conversational connections automatically and illustrate this method with
Twitter data. Specifically, we detail a graph neural network model capable of
representing tweets in a vector space based on their text, hashtags, URLs, and
neighboring tweets. Once tweets are represented, clusters of tweets uncover
conversational contexts. We apply our method to a dataset with 4.5 million
tweets discussing the 2020 US election. We find that even filtered data
contains many different conversational contexts, with users engaging in
multiple contexts. Central users in the contextualized networks differ
significantly from central users in the overall network. This result implies
that standard network analysis on social media data can be unreliable in the
face of multiple conversational contexts. We further demonstrate that dynamic
analysis of conversational contexts gives a qualitative understanding of
conversational flow.
