Ceron, TaniseTaniseCeronBarić, AnaAnaBarićBlessing, AndréAndréBlessingHaunss, SebastianSebastianHaunssKuhn, JonasJonasKuhnLapesa, GabriellaGabriellaLapesaPadó, SebastianSebastianPadóPapay, SeanSeanPapay0009-0006-2330-7349Zauchner, Patricia F.Patricia F.Zauchner2026-06-152026-06-152026https://fis.uni-bamberg.de/handle/uniba/115585The opinions of political actors (e.g., politicians, parties, organizations) expressed through claims are the core elements of political debates and decision-making. Political actors communicate through different channels: parties publish manifestos for major elections, while individual actors make statements on a day-to-day basis as reflected in the media. These two channels offer different approaches for analysis: Manifestos, on the one hand, are useful to characterize the parties’ positions at a global ideological level over time. In contrast, individual statements can be collected to analyze debates in particular policy domains on a fine-grained level, in terms of individual actors and claims. In this article, we summarize a series of studies we have carried out. We apply NLP-driven (semi-)automatic analyses on these two channels and compare their potentials and challenges. The fine-grained analysis yields rich insights into the communication but comes at the cost of three challenges: (a) a substantial hunger for manual annotation, introducing practical hurdles for analysis both within and across languages; (b) difficulties in claim classification arising from the uneven frequency distribution over the theory-based annotation schemas; (c) the need to map actor mentions onto canonical versions. Manifesto-based analysis avoids these challenges to a substantial extent when a more coarse-grained analysis of party positions is sufficient. We highlight the benefits and challenges of both approaches, and conclude by outlining perspectives for addressing the challenges in future research.engClaim identificationdiscourse network analysisparty positioningargument miningconferenceobjecturn:nbn:de:bvb:473-irb-115585x