Digital Pandemic Campaigning (DiPaCa). A content analysis on political actors’ use of Facebook advertisements during the 2021 German Federal Election.

This dataset contains manually coded content analysis data from the Digital Pandemic Campaigning (DiPaCa) project, which analyzed how German political parties and their lead candidates used COVID-19 as a campaign topic in paid Facebook and Instagram advertisements during the 2021 German federal election (Bundestagswahl). The dataset comprises 4,770 political advertisements collected during the hot campaign phase (30 August–27 September 2021). Ads were placed by seven parties (FDP, CDU, SPD, Bündnis 90/Die Grünen, AfD, Die Linke, CSU) and five lead candidates (Christian Lindner, Armin Laschet, Annalena Baerbock, Olaf Scholz). Ads were delivered on Facebook, Instagram, or both. Each advertisement was manually coded using a standardized codebook (Coding scheme is not publicly shareable due to project internal policies, but can be requested from the authors). The dataset contains Meta Ad Library metadata such as ad IDs, ad library links, delivery dates, and platforms, to retrieve all metadata (spending, impressions, etc.) through the Ad Library API. Coded dimensions include: ad format and visual content, mobilization and interaction strategies, information functions, policy topics, personalization, negative campaigning (attack targets, strategies, and stereotyping), positive campaigning (acclaiming), populism (anti-elitism, people-centrism, crisis framing), audience targeting (by age, gender, family status, occupation, religion, voter type, geographic location, and identity groups), ideological positioning, misinformation and conspiracy indicators, and linked content. The unit of analysis is the individual advertisement. The dataset does not contain the original ad text or media content; it contains the coded content analysis variables derived from them.