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

arXiv: https://arxiv.org/abs/2305.08978 · HTML: https://mlsanity.com/paper/2305.08978/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 15 May 2023.

## Builds on (most cited first, 6 of 6)

- [Human language reveals a universal positivity bias](https://mlsanity.com/paper/1406.3855/) (2014)
- [Generalized Word Shift Graphs: A Method for Visualizing and Explaining Pairwise Comparisons Between Texts](https://mlsanity.com/paper/2008.02250/) (2020)
- [Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using Twitter](https://mlsanity.com/paper/2007.12988/) (2020)
- [The Geography of Happiness: Connecting Twitter sentiment and expression, demographics, and objective characteristics of place](https://mlsanity.com/paper/1302.3299/) (2013)
- [The Lexicocalorimeter: Gauging public health through caloric input and output on social media](https://mlsanity.com/paper/1507.05098/) (2015)
- [Mining of health and disease events on Twitter: validating search protocols within the setting of Indonesia](https://mlsanity.com/paper/1608.05910/) (2016)

## Abstract (arXiv)

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.