  Between January 2017 and January 2021, thousands of local news sources in the
United States reported on over 42,000 protests about topics such as civil
rights, immigration, guns, and the environment. Given the vast number of local
journalists that report on protests daily, extracting these events as
structured data to understand temporal and geographic trends can empower civic
decision-making. However, the task of extracting events from news articles
presents well known challenges to the NLP community in the fields of domain
detection, slot filling, and coreference resolution.
  To help improve the resources available for extracting structured data from
news stories, our contribution is three-fold. We 1) release a manually labeled
dataset of news article URLs, dates, locations, crowd size estimates, and 494
discrete descriptive tags corresponding to 42,347 reported protest events in
the United States between January 2017 and January 2021; 2) describe the
semi-automated data collection pipeline used to discover, sort, and review the
144,568 English articles that comprise the dataset; and 3) benchmark a
long-short term memory (LSTM) low dimensional classifier that demonstrates the
utility of processing news articles based on syntactic structures, such as
paragraphs and sentences, to count the number of reported protest events.
