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In real-time, social media data strongly imprints world events, popularculture, and day-to-day conversations by millions of ordinary people at a scalethat is scarcely conventionalized and recorded. Vitally, and absent from manystandard corpora such as books and news archives, sharing and commentingmechanisms are native to social media platforms, enabling us to quantify socialamplification (i.e., popularity) of trending storylines and contemporarycultural phenomena. Here, we describe Storywrangler, a natural languageprocessing instrument designed to carry out an ongoing, day-scale curation ofover 100 billion tweets containing roughly 1 trillion 1-grams from 2008 to2021. For each day, we break tweets into unigrams, bigrams, and trigramsspanning over 100 languages. We track n-gram usage frequencies, and generateZipf distributions, for words, hashtags, handles, numerals, symbols, andemojis. We make the data set available through an interactive time seriesviewer, and as downloadable time series and daily distributions. AlthoughStorywrangler leverages Twitter data, our method of extracting and trackingdynamic changes of n-grams can be extended to any similar social mediaplatform. We showcase a few examples of the many possible avenues of study weaim to enable including how social amplification can be visualized through'contagiograms'. We also present some example case studies that bridge n-gramtime series with disparate data sources to explore sociotechnical dynamics offamous individuals, box office success, and social unrest.

Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using Twitter