2022
DOI: 10.48550/arxiv.2203.03608
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Emotion Regulation and Dynamics of Moral Concerns During the Early COVID-19 Pandemic

Abstract: The COVID-19 pandemic has upended daily life around the globe, posing a threat to public health. Intuitively, we expect that surging cases and deaths would lead to fear, distress and other negative emotions. However, using state-of-the-art methods to measure emotions and moral concerns in social media messages posted in the early stage of the pandemic, we see a counter-intuitive rise in some positive affect. In addition, we measure changes in emotions and moral concerns during notable events, such as the first… Show more

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Cited by 4 publications
(3 citation statements)
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“…To measure the presence of emotions in a tweet we use SpanEmo, a multi-label emotion classification system (Alhuzali and Ananiadou 2021; Guo et al 2022). SpanEmo utilizes BERT encodings of input text and emotion classes, and yields a higher F1 score than base BERT model.…”
Section: Emotion Inferencementioning
confidence: 99%
“…To measure the presence of emotions in a tweet we use SpanEmo, a multi-label emotion classification system (Alhuzali and Ananiadou 2021; Guo et al 2022). SpanEmo utilizes BERT encodings of input text and emotion classes, and yields a higher F1 score than base BERT model.…”
Section: Emotion Inferencementioning
confidence: 99%
“…The advantage of social media datasets over surveybased measurements used in public opinion polls is the ability to feasibly assess issue positions of the larger public without biases inherent in survey self-reports, especially of contentious political issues (e.g., social desirability bias). Large-scale social media datasets have also made possible, the analysis of protest mobilization (Breuer, Landman, and Farquhar 2015;Steinert-Threlkeld 2017;Munn 2021), proliferation of misinformation (Nikolov, Flammini, and Menczer 2020;Rao et al 2021;Chen et al 2021), moral and emotional attitudes (Guo et al 2022;Priniski et al 2021), echo chambers and ideological biases on platforms (Barberá 2015; Wojcieszak et al 2022), among other democratically relevant phenomena. Previous studies (Yardi and Boyd 2010;Garimella et al 2018;Cinelli et al 2021) have also analyzed polarization with respect to wedge issues, including abortion rights in the US.…”
Section: Introductionmentioning
confidence: 99%
“…They can guide our attention and influence our information consumption, beliefs, and our interactions [10,26]. Deep learning has enabled us to extract affective constructs from natural language [7,5], allowing emotion recognition from text at scale [13]. Nevertheless, the need for better performance across various metrics of interest still exists.…”
Section: Introductionmentioning
confidence: 99%