Tweet Emotion Dynamics: Emotion Word Usage in Tweets from US and Canada
This work addresses the need for tools to study emotional expression in social media for researchers in public health, affective science, and psychology, though it is incremental as it builds on existing emotion analysis methods.
The paper tackles the problem of analyzing emotion word usage in tweets by introducing a large geo-located dataset (TUSC) and Tweet Emotion Dynamics (TED) metrics, showing that Canadian tweets have higher valence and lower arousal than US tweets and that the COVID-19 pandemic significantly impacted emotional signatures in 2020.
Over the last decade, Twitter has emerged as one of the most influential forums for social, political, and health discourse. In this paper, we introduce a massive dataset of more than 45 million geo-located tweets posted between 2015 and 2021 from US and Canada (TUSC), especially curated for natural language analysis. We also introduce Tweet Emotion Dynamics (TED) -- metrics to capture patterns of emotions associated with tweets over time. We use TED and TUSC to explore the use of emotion-associated words across US and Canada; across 2019 (pre-pandemic), 2020 (the year the pandemic hit), and 2021 (the second year of the pandemic); and across individual tweeters. We show that Canadian tweets tend to have higher valence, lower arousal, and higher dominance than the US tweets. Further, we show that the COVID-19 pandemic had a marked impact on the emotional signature of tweets posted in 2020, when compared to the adjoining years. Finally, we determine metrics of TED for 170,000 tweeters to benchmark characteristics of TED metrics at an aggregate level. TUSC and the metrics for TED will enable a wide variety of research on studying how we use language to express ourselves, persuade, communicate, and influence, with particularly promising applications in public health, affective science, social science, and psychology.