  With the rise of opioid abuse in the US, there has been a growth of
overlapping hotspots for overdose-related and HIV-related deaths in
Springfield, Boston, Fall River, New Bedford, and parts of Cape Cod. With a
large part of population, including rural communities, active on social media,
it is crucial that we leverage the predictive power of social media as a
preventive measure. We explore the predictive power of micro-blogging social
media website Twitter with respect to HIV new diagnosis rates per county. While
trending work in Twitter NLP has focused on primarily text-based features, we
show that multi-dimensional feature construction can significantly improve the
predictive power of topic features alone with respect STI's (sexually
transmitted infections). By multi-dimensional features, we mean leveraging not
only the topical features (text) of a corpus, but also location-based
information (counties) about the tweets in feature-construction. We develop
novel text-location-based smoothing features to predict new diagnoses of HIV.
