  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.
