Swimming with the Tide? Positional Claim Detection across Political Text Types

Manifestos are official documents of political parties, providing a comprehensive topical overview of the electoral programs. Voters, however, seldom read them and often prefer other channels, such as newspaper articles, to understand the party positions on various policy issues. The natural question to ask is how compatible these two formats (manifesto and newspaper reports) are in their representation of party positioning. We address this question with an approach that combines political science (manual annotation and analysis) and natural language processing (supervised claim identification) in a cross-text type setting: we train a classifier on annotated newspaper data and test its performance on manifestos. Our findings show a) strong performance for supervised classification even across text types and b) a substantive overlap between the two formats in terms of party positioning, with differences regarding the salience of specific issues.