Abstract
Gun related violence is a complex issue and accounts for a large proportion of violent incidents. In the research reported in this paper, we set out to investigate the pro-gun and anti-gun sentiments expressed on a social media platform, namely Twitter, in response to the 2012 Sandy Hook Elementary School shooting in Connecticut, USA. Machine learning techniques are applied to classify a data corpus of over 700,000 tweets. The sentiments are captured using a public sentiment score that considers the volume of tweets as well as population. A web-based interactive tool is developed to visualise the sentiments and is available at this http://www.gunsontwitter.com. The key findings from this research are: (i) There are elevated rates of both pro-gun and anti-gun sentiments on the day of the shooting. Surprisingly, the pro-gun sentiment remains high for a number of days following the event but the anti-gun sentiment quickly falls to pre-event levels. (ii) There is a different public response from each state, with the highest pro-gun sentiment not coming from those with highest gun ownership levels but rather from California, Texas and New York.
| Original language | English |
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| Title of host publication | 2016 IEEE 12th International Conference on e-Science (e-Science) |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| ISBN (Electronic) | 978-1-5090-4273-9 |
| ISBN (Print) | 978-1-5090-4274-6 |
| DOIs | |
| Publication status | Published - 06 Mar 2017 |
| Event | 2016 IEEE 12th International Conference on eScience - Baltimore, United States Duration: 23 Oct 2016 → 27 Oct 2016 http://escience-2016.idies.jhu.edu/ |
Conference
| Conference | 2016 IEEE 12th International Conference on eScience |
|---|---|
| Country/Territory | United States |
| City | Baltimore |
| Period | 23/10/2016 → 27/10/2016 |
| Internet address |
Access to Document
- A Machine Learning Analysis of Twitter Sentiment to the Sandy Hook Shootings
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