Advancing Gun Violence Research with Social Media Data: A Computational Analysis of Gun Ownership (Preprint)

Abstract

Social media data represent a potentially valuable source of information to support gun violence research. Strengthening the empirical and methodological foundations for using social media data in this context is important for advancing the future application of social media data to gun violence research.

We assess the extent to which social media-based estimates are able to accurately capture geographic variability in firearms-related outcomes using firearm ownership as a test.

We use Twitter data from 2019-2021 and state of the art computational methods to construct a machine learning model of firearm ownership. We create state-specific estimates of ownership and assess these estimates by comparing them to benchmark measures.

Methodologically, our study highlights the importance of large draws from social media data when location identification is paramount. Our analytic approach for modeling firearm ownership using machine learning and adjusting estimates using an inferred demographic provide examples of how these techniques can be used and expanded in future gun violence research. Empirically, we find a strong positive correlation between Twitter-based estimates of gun ownership and benchmark ownership estimates. For states meeting a threshold requirement of a minimum of 100 labeled Twitter users, the Pearson’s and Spearman’s correlations are 0.63 (p<0.001) and 0.64 (p <0.001), respectively.

Our findings underscore the potential of social media data for providing new windows into firearm behavior and outcomes, especially when measures from traditional data sources are limited or unavailable. Social media data carry analytical challenges when used for research purposes. Careful attention to them, as well as to ethical standards for use, is essential as the frontiers of social media data’s use in research are explored.