"Every day, 100 Americans are killed with guns and hundreds more are shot and injured. The effects of gun violence extend far beyond these casualties—gun violence shapes the lives of millions of Americans who witness it, know someone who was shot, or live in fear of the next shooting." -EveryTown Research Group (https://everytownresearch.org/gun-violence-america/)
The Current Process of U.S. Firearm Checks
1) Firearm Buyer: Fills out an ATF Form 4473 with: name, age, address, place of birth, race, citizenship, Social Security (optional), as well as the following questions:
2) Firearm Buyer answers the following questions [five different parameters]: (1) Have you ever been convicted of a felony?, (2) Have you ever been convicted of a misdemeanor crime of domestic violence?, (3) Are you an unlawful user of, or addicted to, marijuana or any other depressant, stimulant, narcotic drug, or any other controlled substance?, (4) Are you a fugitive from justice?, and (5) Have you ever been committed to a mental institution?.
2) Firearm Vendor: Submits the information to law enforcement officials via a toll-free phone line or over the internet, and the agency checks the applicant's info against databases.
3) Law enforcement: Conducts background check with the submitted form (can take minutes). Law enforcement officials will approve or deny a candidates request to purchase a firearm. When is someone Denied the Right to Firearms?: if you are convicted of a crime punishable by imprisonment; if you are convicted of a violent misdemeanor; if you are an addict of any controlled substance; if you are committed to a mental institution; if you are an illegal immigrant; if you are harassing, stalking, or threatening an intimate partner; if you renounced your U.S. citizenship.
What it does
DeepCheck is a modern, accessible, and dynamic machine-learning powered web portal to help law enforcement officials flag candidates and perform more robust background checks.
In conjunction with the current five foundational parameters in background checks (listed in Inspiration section), DeepCheck introduces the concept of utilizing candidates' public social media activity to flag them for further law enforcement investigation. Public social media data should not be used as a determining factor in adjudicating eligibility for firearms possession. Rather, DeepCheck is a proof of concept of how vitriolic social media activity might be discovered using machine intelligence and later evaluated by law enforcement officials to complement or augment accepted background checks.
The current social media application we have decided to utilize is Twitter. DeepCheck gathers a candidates' recent (up to 500) status updates (or tweets), retweets, and favorites. Upon gathering the data, DeepCheck runs a natural language processing algorithm which implements sentiment analysis to spotlight offensive language, hate speech, and any encouragement of violent crime. The purpose of this in-depth analysis is to catch hidden sentiments or motives in individuals who do not have a past history with crime or law enforcement.
DeepCheck also includes an interactive portal for firearm vendors to keep a log of all previous customers which have applied with them, an online ATF application as (opposed to the traditional pen-to-paper), an alerts/help center, and a personalized profile account.
How we built it
Sample of the Jupyter Notebook Here: https://github.com/katiehouse3/deep-check/blob/master/DeepCheck.ipynb
Challenges we ran into
Accomplishments that we're proud of
Our model has 84% accuracy on the test data!! We are proud of channeling our frustration for lack of gun control into a productive technical project which has the potential to increase the effectiveness of background checks and help reduce the amount of gun violence that occurs from legally purchased firearms.
What we learned
We learned Natural Language Processing for the first-time and dived into the world of data gathering and organization. It was extremely interesting to see the impact of the data's quality on the prediction results the ML model was outputting.
What's next for DeepCheck
We hope that DeepCheck will spread awareness of a flagging system to improve upon the current background check with real-time indications of at-risk candidates. We are deeply saddened by the amount of gun violence that occurs on a daily rate in our country and the innocent civilians who are affected. DeepCheck has the potential to delve into further fields such as training the machine learning model to look for other sentiments such as mood disorders, depression, and suicide risk in order to decrease suicides by gun. Overall the purpose of DeepCheck is to help prevent gun violence in the United States.