An assessment of measuring local levels of homelessness through proxy social media signals

TL;DR

This study explores the potential of using geotagged Twitter data to estimate and monitor homelessness levels across US states, revealing correlations and linguistic trends that reflect real-world conditions.

Contribution

It demonstrates the feasibility of using social media signals, specifically Twitter activity, as a proxy for measuring homelessness at the state level over time.

Findings

Twitter tweet counts correlate with homelessness volume but not population density.

Linguistic content of tweets changes over time, indicating shifts in public discourse and sentiment.

User account analysis reveals different social media usage patterns related to homelessness.

Abstract

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.