  In real-time, social media data strongly imprints world events, popular
culture, and day-to-day conversations by millions of ordinary people at a scale
that is scarcely conventionalized and recorded. Vitally, and absent from many
standard corpora such as books and news archives, sharing and commenting
mechanisms are native to social media platforms, enabling us to quantify social
amplification (i.e., popularity) of trending storylines and contemporary
cultural phenomena. Here, we describe Storywrangler, a natural language
processing instrument designed to carry out an ongoing, day-scale curation of
over 100 billion tweets containing roughly 1 trillion 1-grams from 2008 to
2021. For each day, we break tweets into unigrams, bigrams, and trigrams
spanning over 100 languages. We track n-gram usage frequencies, and generate
Zipf distributions, for words, hashtags, handles, numerals, symbols, and
emojis. We make the data set available through an interactive time series
viewer, and as downloadable time series and daily distributions. Although
Storywrangler leverages Twitter data, our method of extracting and tracking
dynamic changes of n-grams can be extended to any similar social media
platform. We showcase a few examples of the many possible avenues of study we
aim to enable including how social amplification can be visualized through
'contagiograms'. We also present some example case studies that bridge n-gram
time series with disparate data sources to explore sociotechnical dynamics of
famous individuals, box office success, and social unrest.
