  Recent studies suggest social media activity can function as a proxy for
measures of state-level public health, detectable through natural language
processing. We present results of our efforts to apply this approach to
estimate homelessness at the state level throughout the US during the period
2010-2019 and 2022 using a dataset of roughly 1 million geotagged tweets
containing the substring ``homeless.'' Correlations between
homelessness-related tweet counts and ranked per capita homelessness volume,
but not general-population densities, suggest a relationship between the
likelihood of Twitter users to personally encounter or observe homelessness in
their everyday lives and their likelihood to communicate about it online. An
increase to the log-odds of ``homeless'' appearing in an English-language
tweet, as well as an acceleration in the increase in average tweet sentiment,
suggest that tweets about homelessness are also affected by trends at the
nation-scale. Additionally, changes to the lexical content of tweets over time
suggest that reversals to the polarity of national or state-level trends may be
detectable through an increase in political or service-sector language over the
semantics of charity or direct appeals. An analysis of user account type also
revealed changes to Twitter-use patterns by accounts authored by individuals
versus entities that may provide an additional signal to confirm changes to
homelessness density in a given jurisdiction. While a computational approach to
social media analysis may provide a low-cost, real-time dataset rich with
information about nationwide and localized impacts of homelessness and
homelessness policy, we find that practical issues abound, limiting the
potential of social media as a proxy to complement other measures of
homelessness.
