  Social media features substantial stylistic variation, raising new challenges
for syntactic analysis of online writing. However, this variation is often
aligned with author attributes such as age, gender, and geography, as well as
more readily-available social network metadata. In this paper, we report new
evidence on the link between language and social networks in the task of
part-of-speech tagging. We find that tagger error rates are correlated with
network structure, with high accuracy in some parts of the network, and lower
accuracy elsewhere. As a result, tagger accuracy depends on training from a
balanced sample of the network, rather than training on texts from a narrow
subcommunity. We also describe our attempts to add robustness to stylistic
variation, by building a mixture-of-experts model in which each expert is
associated with a region of the social network. While prior work found that
similar approaches yield performance improvements in sentiment analysis and
entity linking, we were unable to obtain performance improvements in
part-of-speech tagging, despite strong evidence for the link between
part-of-speech error rates and social network structure.
