A Decade of Tweets: Visualizing Racial Sentiments Towards Minoritized Groups in the United States Between 2011 and 2021

Abstract

Background: Research has demonstrated the negative impact of racism on health, yet the measurement of racial sentiment remains challenging. This paper provides practical guidance in using social media data for measuring public sentiment. Methods: We describe the main steps of such research including data collection, data cleaning, binary sentiment analysis, and visualization of findings. We randomly sampled 55,844,310 publicly available tweets from January 1, 2011 – December 31, 2021 using Twitter’s Application Programming Interface. We restricted analyses to U.S. tweets in English using one or more 90 race-related keywords. We used Support Vector Machine, a supervised machine learning model, for sentiment analysis. Results: The proportion of tweets referencing racially minoritized groups that was negative increased at the county, state, and national levels with a 16.5% increase at the national level from 2011-2021. Tweets referencing Black and Middle Eastern people consistently had the highest proportion of negative sentiment compared to all other groups. Stratifying temporal trends by racial and ethnic groups revealed unique patterns reflecting historical events specific to each group such as the killing of George Floyd regarding sentiment against Black people, discussions of the border crisis near the 2018 midterm elections against Latinx people, and the emergence of COVID-19 against Asian people. Conclusions: This study demonstrates the utility of social media data as a quantitative means to measure racial sentiment over time and place. This approach can be extended to a range of public health topics to investigate how changes in social and cultural norms impact behaviors and policy.

Measuring Racial Sentiment Using Social Media Is Harder Than It Seems

“…Social media, and the internet more broadly, presents a historically unprecedented opportunity to assess the speech of millions of individuals across social classes, ages, nationalities, and racial or ethnic groups, making it a tempting resource for epidemiologists interested in understanding broad-scale public opinions. In the current issue, Nguyen et al 1 present a descriptive study of race-related content on Twitter (now renamed X) over the past decade, reporting patterns of negative sentiment language and concluding the relative frequency has increased over time. They provide a detailed explanation of how to curate a dataset from social media and suggestions for the types of quantitative data analyses that can be done with this data.…”