An Association of Twitter Derived Measures of Discrimination and Race-Motivated Hate Crimes
DOI:
https://doi.org/10.47611/jsrhs.v11i3.2819Keywords:
discrimination, racism, hate crimes, data science, Twitter Streaming API, prejuidice, hate speechAbstract
In 2020, there was a sharp rise in Anti-Asian American hate crimes. While the causes are still being explored, one of the main reasons this occurred was due to the rise in Anti-Asian American rhetoric connected to the COVID-19 pandemic. This exemplifies how prejudices often can turn into violence, more specifically hate crimes. And hateful speech, oftentimes spread through the internet, can help spread and deepen that prejudice to a wider audience. In previous research, prejuidice and discrimination has largely been studied through self-reported measures. While those studies are necessary, they are susceptible to many pitfalls and should be supplemented with statistics-based measures. This study employs one of these emerging measures. This study used the Twitter Streaming API to collect tweets containing a racial slur, an extension of hateful speech, geo-located from users in the United States over a period of two months. With these, this study compared statewide Twitter racial slur usage and statewide average sentiment polarity of the tweets with race-motivated hate crimes from the state. Though no relationship was found between statewide sentiment expressed and race-motivated hate crimes, this study did find a significant positive relationship between statewide Twitter slur usage and hate crimes. These findings support previous studies that have connected Internet-based measures of discrimination with aspects of general well-being. and even specifically hate crimes. This study shows the potential of using data derived from social media to create cost effective and expansive indicators of an area’s social climate around a topic, even race.
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