  The rich and dynamic information environment of social media provides
researchers, policy makers, and entrepreneurs with opportunities to learn about
social phenomena in a timely manner. However, using these data to understand
social behavior is difficult due to heterogeneity of topics and events
discussed in the highly dynamic online information environment. To address
these challenges, we present a method for systematically detecting and
measuring emotional reactions to offline events using change point detection on
the time series of collective affect, and further explaining these reactions
using a transformer-based topic model. We demonstrate the utility of the method
by successfully detecting major and smaller events on three different datasets,
including (1) a Los Angeles Tweet dataset between Jan. and Aug. 2020, in which
we revealed the complex psychological impact of the BlackLivesMatter movement
and the COVID-19 pandemic, (2) a dataset related to abortion rights discussions
in USA, in which we uncovered the strong emotional reactions to the overturn of
Roe v. Wade and state abortion bans, and (3) a dataset about the 2022 French
presidential election, in which we discovered the emotional and moral shift
from positive before voting to fear and criticism after voting. The capability
of our method allows for better sensing and monitoring of population's
reactions during crises using online data.
