a summary of the data we acquired.

  [**Source** ]{}                         
  ----------------- ------- ------ ------ -------
  [*Twitter*]{}         825   107M    26M       –
  [*Reddit*]{}        1,257    45M   3.3M       –
  [*News*]{}              –      –      –   3.3 M

  : Summary of the final data collection.[]{data-label="tbl:datasets"}

[**Acquiring Twitter data.**]{} We first queried the bootstrap terms, and retrieved 958K messages posted by 413K users. We then expanded the query by manually annotating 2088 terms that appeared more frequently than an arbitrary threshold (ranging from 75 for tri-grams to 300 for uni-grams, which are typically less precise than tri-grams and noisier at lower frequencies), after removing stopwords using the Python NLTK package. We found an extra 612 terms. We queried these terms, growing our collection by 55M tweets posted by 12.5M users. The resulting dataset contains on average 4.5M tweets per month. Since we used the Twitter Decahose (a 10% sample of all Twitter content), we estimate this collection is in fact representative of a larger set of roughly 45M tweets per month. Finally, we retrieved tweets matching the 163 external hate terms (based on HateBase), resulting in an additional 51.6M tweets by 13.7M users. Altogether, we collected over 1TB of raw Twitter data.

[**Acquiring Reddit data.** ]{} We again began 