Project Abstract

Defunding the police is a polarizing topic that is on the rise in the United States. Public opinion is generally divided due to controversial recent events that have involved law enforcement officers (LEOs). However, how we perceive or not violence around us likely contributes to our own assessment of LEOs’ necessity. In this project, we seek to carry out an analysis about the interplay between these two factors to study if violent incidents, whether from LEOs or criminals, shape the opinion of New York City (NYC) inhabitants.

Project Description & Overview

Recently, public opinion has been inflamed by controversial police actions, in particular, by the use of excessive force to maintain public order. However, other factors are at play in defining public opinion to police. On one hand, violent episodes might have generated opposition to police. On the other hand, an increase in local crime could have fueled the demand for stricter law enforcement.

In this project, we seek to understand how local crime and incidents of police brutality contribute to shape public opinion. NYC is a great framework to investigate this relationship, due to its abundant data. To this end, we will undertake a massive data collection effort, by cataloguing tweets, which will allow us to track public opinion citywide using machine learning techniques for sentiment analysis. To obtain an accurate description of citywide violence, geolocated data on crimes will be collected from NYC Open Portal and NYPD databases. For the brutality episodes involving LEOs, we will build a dataset relying on Washington Post fatal police shootings and crowdsource databases.

We will test hypotheses that entail the driving forces behind public opinion: i) “Does the increase in crime rate lead to an increase in police supporters?”; and ii) “Does an increase in police brutality lead to higher support for defunding of police?”. We will apply parametric and non-parametric statistical tools to test our hypotheses and elucidate the emergence of spatio-temporal patterns. The results of the study will shed light on drivers of public-police relations and provide evidence to reform policy-making.

Datasets

• Local Crime
  • Geo-located local crimes from 2006 to 2020 will be obtained from the NYC Open Data Portal 
  • NYPD keeps records, on an hourly basis and on a street level of all the various crimes committed in NYC from January 2020 up to date
• Police Brutality
  • Washington Post records police killings from 2015 up to date with geolocation 
  • Official CAPstat registers payroll info, disciplinary summaries, and federal lawsuits from 2015 to 2018
  • Mapping Police Violence Initiative from 2013 up to date 
  • Fatal Encounters Initiative from 2000 up to date 
• Tweets
  • Tweets will be collected using the official Twitter API with the help of Python package Tweepy and through The Ohio State University software Hydrator 
  • Older tweets will also be collected through web scraping through Twint GitHub initiative.

The Sentiment Lexicon dictionary from the University of Pittsburgh and the dictionary of sentiment words from Bing Liu and collaborators (University of Illinois Chicago) will be used for the sentiment analysis. 

Competencies

• Statistics
• Data extraction and web scraping (preferably Python or R)
• Data analysis and visualization (using Python, R or Matlab)
• Programming (preferably Python, R, or Matlab)

We are looking for highly motivated students with a passion to explore and learn new concepts and ideas, and with an interest in social media, sentiment analysis, and data science. 

Learning Outcomes & Deliverables

1. Students will learn data collection and pre-processing methods and their importance.
2. Students will be trained in the scientific method approach and hypothesis testing, and they will learn data modeling tools for analysis purposes.
3. Students will learn to apply traditional tools of temporal analysis and information theory.

Students

Lingxuan Bu, Ruoqing(Rachel) Lin, Xiangyu Ying