Unleash Britain’s Potential (To Go Negative): Campaign Negativity in the 2017 and 2019 UK General Elections on Facebook

Abstract

Negative campaigning has long concerned scholars because of the potential effects on the electorate and on democracy. Most scholarship has focused on single-election studies in the United States, whereas less is known about how campaigns go on the attack in the UK, and few compare two elections. Drawing from a dataset of Facebook posts by parties and leaders in Great Britain during the five weeks of campaigning in the 2017 and 2019 General Elections (N = 3560), we use supervised machine learning to categorise posts as negative campaigning and distinguish between attacks focused on issues and attacks on candidates’ images. Our findings show that the 2019 election was more negative than in 2017, and that larger parties were more inclined to adopt attacks as a campaign strategy. Moreover, we found that party accounts posted more attack messages than leader accounts and were more focused on attacking based on issues, rather than personal character or image. Finally, we found that attack messages elicit stronger engagement from audiences, with attack messages receiving more attention, particularly attacks on image.