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Over the last decade, Twitter has emerged as one of the most influentialforums for social, political, and health discourse. In this paper, we introducea massive dataset of more than 45 million geo-located tweets posted between2015 and 2021 from US and Canada (TUSC), especially curated for naturallanguage analysis. We also introduce Tweet Emotion Dynamics (TED) -- metrics tocapture patterns of emotions associated with tweets over time. We use TED andTUSC to explore the use of emotion-associated words across US and Canada;across 2019 (pre-pandemic), 2020 (the year the pandemic hit), and 2021 (thesecond year of the pandemic); and across individual tweeters. We show thatCanadian tweets tend to have higher valence, lower arousal, and higherdominance than the US tweets. Further, we show that the COVID-19 pandemic had amarked impact on the emotional signature of tweets posted in 2020, whencompared to the adjoining years. Finally, we determine metrics of TED for170,000 tweeters to benchmark characteristics of TED metrics at an aggregatelevel. TUSC and the metrics for TED will enable a wide variety of research onstudying how we use language to express ourselves, persuade, communicate, andinfluence, with particularly promising applications in public health, affectivescience, social science, and psychology.

Tweet Emotion Dynamics: Emotion Word Usage in Tweets from US and Canada