RT Research Data
T1 Understanding Online Hate Speech as a Motivator and Predictor of Hate Crime, Los Angeles, California, 2017-2018
A1 Cahill, Meagan
A2 Burnap, Pete
A2 Javed, Amir
A2 Liu, Han
A2 Lu, Hui
A2 Migacheva, Katya
A2 Sutherland, Alex
A2 Taylor, Jirka
A2 Williams, Matthew
LA English
PP Erscheinungsort nicht ermittelbar
PB [Verlag nicht ermittelbar]
YR 2021
UL https://krimdok.uni-tuebingen.de/Record/1840038004
AB In the United States, a number of challenges prevent an accurate assessment of the prevalence of hate crimes in different areas of the country. These challenges create huge gaps in knowledge about hate crime--who is targeted, how, and in what areas--which in turn hinder appropriate policy efforts and allocation of resources to the prevention of hate crime. In the absence of high-quality hate crime data, online platforms may provide information that can contribute to a more accurate estimate of the risk of hate crimes in certain places and against certain groups of people. Data on social media posts that use hate speech or internet search terms related to hate against specific groups has the potential to enhance and facilitate timely understanding of what is happening offline, outside of traditional monitoring (e.g., police crime reports). This study assessed the utility of Twitter data to illuminate the prevalence of hate crimes in the United States with the goals of (i) addressing the lack of reliable knowledge about hate crime prevalence in the U.S. by (ii) identifying and analyzing online hate speech and (iii) examining the links between the online hate speech and offline hate crimes. The project drew on four types of data: recorded hate crime data, social media data, census data, and data on hate crime risk factors. An ecological framework and Poisson regression models were adopted to study the explicit link between hate speech online and hate crimes offline. Risk terrain modeling (RTM) was used to further assess the ability to identify places at higher risk of hate crimes offline.
K1 Internet
K1 Disability
K1 Gender
K1 Hate crimes
K1 Hate Speech
K1 Race
K1 Religion
K1 sexual preference
K1 Social Media
K1 Forschungsdaten
DO 10.3886/ICPSR37470.v1