  Social media offers a unique lens to observe large-scale, spatial-temporal
patterns of users reactions toward critical events. However, social media use
varies across demographics, with younger users being more prevalent compared to
older populations. This difference introduces biases in data
representativeness, and analysis based on social media without proper
adjustment will lead to overlooking the voices of digitally marginalized
communities and inaccurate estimations. This study explores solutions to
pinpoint and alleviate the demographic biases in social media analysis through
a case study estimating the public sentiment about COVID-19 using Twitter data.
We analyzed the pandemic-related Twitter data in the U.S. during 2020-2021 to
(1) elucidate the uneven social media usage among demographic groups and the
disparities of their sentiments toward COVID-19, (2) construct an adjusted
public sentiment measurement based on social media, the Sentiment Adjusted by
Demographics (SAD) index, to evaluate the spatiotemporal varying public
sentiment toward COVID-19. The results show higher proportions of female and
adolescent Twitter users expressing negative emotions to COVID-19. The SAD
index unveils that the public sentiment toward COVID-19 was most negative in
January and February 2020 and most positive in April 2020. Vermont and Wyoming
were the most positive and negative states toward COVID-19.
