No open-access location is known for this record yet.
No open-access location is known for this record yet.
A seemingly inescapable feature of the digital age is that people choosing to devote their lives to politics must now be ready to face a barrage of insults and disparaging comments targeted at them through social media. This article represents an effort to document this phenomenon systematically. We implement machine learning models to predict the incivility of about 2.2 m messages addressed to Canadian politicians and US Senators on Twitter. Specifically, we test whether women in politics are more heavily targeted by online incivility, as recent media reports suggested. Our estimates indicate that roughly 15% of public messages sent to Senators can be categorized as uncivil, whereas the proportion is about four points lower in Canada. We find evidence that women are more heavily targeted by uncivil messages than men, although only among highly visible politicians.
Loading…
Stored only when both papers are in the corpus — 233 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.