Cross-National Evidence of Disproportionate Media Visibility for the Radical Right in the 2024 European Elections

TL;DR

This study examines cross-national media visibility of political families in the 2024 European Parliament elections across five countries using a hybrid pipeline that combines large-scale online news collection, LLM- and fuzzy-matching–based entity extraction, and manual validation to map mentions to European Parliament groups and five broad leanings. It analyzes 21,528 EU-election news items from major outlets, identifying 10,292 items with explicit political entities and aggregating by country and seat-weighted Europe-wide shares. The key finding is a disproportionate and increasing emphasis on Radical Right actors, which is not fully explained by prior results, polling, or government participation, and which persists across highly popular and niche outlets, suggesting a systemic visibility bias in the digital news ecosystem. The work highlights implications for democratic competition, editorial decision-making, and the need for further research on tone and framing to understand causal effects on electoral outcomes.

Abstract

This study provides a systematic comparative analysis of media visibility of different political families during the 2024 European Parliament elections. We analyzed close to 21,500 unique news from leading national outlets in Austria, Germany, Ireland, Poland, and Portugal - countries with diverse political contexts and levels of media trust. Combining computational and human classification, we identified parties, political leaders, and groups from the article's URLs and titles, and clustered them according to European Parliament political families and broad political leanings. Cross-country comparison shows that the Mainstream and the Radical Right were mentioned more often than the other political groups. Moreover, the Radical Right received disproportionate attention relative to electoral results (from 2019 or 2024) and electoral projections, particularly in Austria, Germany, and Ireland. This imbalance increased in the final weeks of the campaign, when media influence on undecided voters is greatest. Outlet-level analysis shows that coverage of right-leaning entities dominated across news sources, especially those generating the highest traffic, suggesting a structural rather than outlet-specific pattern. Media visibility is a central resource, and this systematic mapping of online coverage highlights how traditional media can contribute to structural asymmetries in democratic competition.

Figures (11)

• Figure 1: Methodological Pipeline. News extraction and classification was done in four sequential steps. (1) Following electoral-related keyword searches on Media Cloud, news articles were collected. (2) Through automatic classification, identified political entities (politicians, parties, political leanings) were matched to their respective national parties. Only news with mentions in their titles or URLs were included. (3) National entities were matched to their European Parliament groups (as defined in 2019) and then to five main political leanings (from left to right: Radical Left - dark red, Mainstream Left - light red, Greens - green, Mainstream Right - light blue, and Radical Right - dark blue). The new far-right formations emerging in 2024 -- Patriots for Europe and Sovereignists -- were classified under the Identity and Democracy group for comparability. (4) Frequency of media mentions were compared to the 2019 and 2024 electoral results and to the 2024 pooled voting intentions.
• Figure 2: Methodological Pipeline - News Collection. Media Sources were selected from Semrush Top 20 Newspapers for each country and excluding social media and search engine pages (e.g., "YouTube.com"). News articles were collected from these sources using two sets of queries (yellow and blue), derived from three lists of terms in English, German, Polish, and Portuguese. The titles and URLs of all news articles from the domains identified through the Semrush lists, and containing at least one query from each set, were gathered to form the final Europe-related News Database. Videos and news in other languages, returned by Media Cloud, were filtered out and not included.
• Figure 3: Methodological Pipeline - Entities Extraction. A) All news headlines and their respective URLs were processed using the ChatGPT-4o API to identify political entities -- such as politicians (e.g., Hildergard Bentele), political parties (e.g., AfD), and political leanings (e.g., the left). B) For mentions of political leanings (yellow), the news corpus was further searched to determine the specific political entity referred to under that label. C) Fuzzy matching was applied to detect entities missed by ChatGPT, and a manual validation of 150 news items was conducted to assess extraction accuracy.
• Figure 4: Average weekly number of news items from Media Cloud (solid line) and the subset with mapped entities (dashed line). Vertical lines mark two-week intervals. A 3-week running window was used to compute the averages.
• Figure 5: a) Share of political entity mentions in the news, grouped by major European political families (as defined in Scheme \ref{['fig:methodology']}), for each country. b) Difference between media attention (Panel a) and the 2019 European Parliament seat distribution (triangles). c) Difference between media attention and pre-election seat projections from EUobserver polls (crosses) euobserver2025euelections. d) Difference between media attention and the final 2024 seat distribution (circles). Symbols within the light grey area fall within one standard deviation of the cross-country mean difference; those in the white area are within two standard deviations.