by querying the bootstrap terms, and retrieved 3K posts with 140K comments written by 49K users. We then expanded the query by selecting high-frequency terms (thresholds ranging from 50 to 300 as these data were sparser than Twitter) across all posts and comments, and manually annotating them. Given that the Reddit Search API normalizes terms before running a query, we did not keep different inflections of the same terms. We annotated 4272 terms, and found 1002 related to hate and counter-hate speech. We queried these terms, and retrieved an extra 300K posts with 41M comments written by 3.1M users. Finally, we queried the external hate terms. Altogether, we collected 337K posts with 45M comments written by roughly 3.3M users.

[**Acquiring news data.**]{} We used GDELT’s Global Knowledge Graph (GKG), as it provides the list of news articles covering each event in their database. This allowed us to compute the overall volume of news per day, amounting to over 130M URLs over our 19 months period of interest.

+-----------------+-----------------+-----------------+-----------------+
| Theme           | Paraphrased     | Theme           | Paraphrased     |
|                 | message/comment |                 | message/comment |
|                 | (s)             |                 | (s)             |
+:================+:================+:================+:================+
| Blames          | “[*Ban Muslims, | Defends         | 