Deploying Social Media Data to Inform Gun Policy

Overview

This study will investigate the use of data from social media posts to measure gun-related outcomes in cities or states, and improve the data available to support rigorous gun policy research.

Grant Amount: $569,970

Organization: Georgetown University

Investigator: Lisa Singh, professor, Department of Computer Science, and research professor, Massive Data Institute, Georgetown University

Expected Completion Date: mid-2021

Project Summary

Purpose

This project seeks to improve the data available to understand gun-related outcomes and support rigorous gun policy research through the novel use of social media data.

Approach

  • Specific Aims:
    • Develop estimates of gun-related mortality and benchmark against estimates based on data from the National Vital Statistics Systems and the Centers for Disease Control.
    • Develop algorithms to predict gun ownership status.
  • Use the large repository of Twitter data housed at Georgetown’s Massive Data Institute (MDI) as primary data source.
  • Collect additional social media data using keywords and hashtags related to gun violence.
  • Conduct a manual review of gun violence/mortality-related tweets and code each tweet as identifying a gun-related homicide, gun-related suicide, or any gun-related death.
  • Using manually labelled data as training data, develop a classifier to identify tweets discussing gun-related deaths.
  • Develop a second classifier that determines type of gun death.
  • Develop predictive algorithms for gun ownership status from Twitter posts.
  • Conduct validation testing throughout the study.

Significance

Build a foundation for understanding for how social media data may be used—alone or with other data resources—to improve gun policy research.

Investigator Bio

Lisa Singh is a professor in the Department of Computer Science, and research professor at the Massive Data Institute, of Georgetown University. Dr. Singh has authored or co-authored over 70 peer-reviewed publications and book chapters related to data-centric computing. Current projects include learning from public, open-source big data to advance social science research on human behavior and opinion; identifying and quantifying noise and other forms of poor-quality information (including misinformation) on social media; developing methods and tools to better understand forced movement due to conflict; and studying privacy on the web.