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Hi all,

I'm giving a guest lecture in a couple days on the intersection of Social
Network Analysis and Natural Language Processing, particularly as applied
toward the analysis of social media datasets. I know there's been a lot of
activity lately at the intersection of SNA and NLP and I'd love to see a
few pointers to some good quality work in this area.

Additionally, I'm hoping to flesh out a few high-level approaches for
merging the two in meaningful ways. Here are a few examples:

1) Use content-based factors (extracted via NLP techniques) as well as
SNA-based metrics as independent variables that predict an outcome of
interest (e.g., quality of content, such as was done in Agichtein,
Castillo, et al. 2008).

2) Use NLP to help define the edges in a network (e.g., "link polarity" as
performed by Kale 2007).

3) Use a 2-step filtering process:
3a) Use SNA to identify network clusters and then use NLP on the corpus
created by those within each cluster (e.g., Marc Smith's graphs on the
NodeXL Graph Gallery where keywords are overlaid on the network clusters)
3b) Use NLP to identify subsets of "relevant" content whose authors are
then analyzed via SNA.

4) SNA helps in disambiguating words (e.g., when one network cluster uses
the term "jaguar" they typically mean the sports team, while another
network cluster typically means the car).

Other thoughts on high-level strategies would be welcome as well.

Derek Hansen
Brigham Young University

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