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Gun related violence is a complex issue and accounts for a large proportionof violent incidents. In the research reported in this paper, we set out toinvestigate the pro-gun and anti-gun sentiments expressed on a social mediaplatform, namely Twitter, in response to the 2012 Sandy Hook Elementary Schoolshooting in Connecticut, USA. Machine learning techniques are applied toclassify a data corpus of over 700,000 tweets. The sentiments are capturedusing a public sentiment score that considers the volume of tweets as well aspopulation. A web-based interactive tool is developed to visualise thesentiments and is available at http://www.gunsontwitter.com. The key findingsfrom this research are: (i) There are elevated rates of both pro-gun andanti-gun sentiments on the day of the shooting. Surprisingly, the pro-gunsentiment remains high for a number of days following the event but theanti-gun sentiment quickly falls to pre-event levels. (ii) There is a differentpublic response from each state, with the highest pro-gun sentiment not comingfrom those with highest gun ownership levels but rather from California, Texasand New York.

A Machine Learning Analysis of Twitter Sentiment to the Sandy Hook Shootings