Does Negativity Pay Off? The Impact of Political Party Attributes on the Use and Performance of Negative Campaigning in Digital Political Advertising
Authors: Paulke, H., Kruschinski, S., Bene, M., Haßler, J., Jackson, D., Lilleker, D., Magin, M., Russmann, U.
Journal: Social Science Computer Review
Publication Date: 01/01/2026
eISSN: 1552-8286
ISSN: 0894-4393
DOI: 10.1177/08944393261469406
Abstract:While research has shown how different political actors adapt engagement-triggering strategies in organic election campaigns on social media platforms, we know little about how such strategies are used in paid political advertising and how they affect algorithmic ad performance. To address this gap, this study analyzes how party attributes shape spending on, impressions of, and the cost efficiency of negative campaigning in digital political advertising. Manually content-analyzing all Facebook ads from 48 parties across 10 countries during the European Parliament election in 2019, we find that, on average, parties do not allocate more resources to negative ads, which also do not achieve higher impression counts and are even associated with higher costs per 1,000 impressions (cost efficiency). However, party characteristics matter: Whereas opposition parties invest more in negative campaigning and consequently gain more impressions than government parties, negativity only pays off in terms of cost efficiency for extreme parties. Our findings contribute to a better understanding of the interplay between advertiser characteristics and content strategies in shaping ad use and performance within commercial marketing infrastructure, emphasizing the need for greater transparency to ensure fair competition in digital political advertising.
Source: Scopus
Does Negativity Pay Off? The Impact of Political Party Attributes on the Use and Performance of Negative Campaigning in Digital Political Advertising
Authors: Paulke, H., Kruschinski, S., Bene, M., Hassler, J., Jackson, D., Lilleker, D., Magin, M., Russmann, U.
Journal: SOCIAL SCIENCE COMPUTER REVIEW
Publication Date: 16/07/2026
eISSN: 1552-8286
ISSN: 0894-4393
DOI: 10.1177/08944393261469406
Source: Web of Science