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The rich and dynamic information environment of social media providesresearchers, policy makers, and entrepreneurs with opportunities to learn aboutsocial phenomena in a timely manner. However, using these data to understandsocial behavior is difficult due to heterogeneity of topics and eventsdiscussed in the highly dynamic online information environment. To addressthese challenges, we present a method for systematically detecting andmeasuring emotional reactions to offline events using change point detection onthe time series of collective affect, and further explaining these reactionsusing a transformer-based topic model. We demonstrate the utility of the methodby successfully detecting major and smaller events on three different datasets,including (1) a Los Angeles Tweet dataset between Jan. and Aug. 2020, in whichwe revealed the complex psychological impact of the BlackLivesMatter movementand the COVID-19 pandemic, (2) a dataset related to abortion rights discussionsin USA, in which we uncovered the strong emotional reactions to the overturn ofRoe v. Wade and state abortion bans, and (3) a dataset about the 2022 Frenchpresidential election, in which we discovered the emotional and moral shiftfrom positive before voting to fear and criticism after voting. The capabilityof our method allows for better sensing and monitoring of population'sreactions during crises using online data.

The Pulse of Mood Online: Unveiling Emotional Reactions in a Dynamic Social Media Landscape