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Pre-trained language models (PLM) based on transformer neural networksdeveloped in the field of natural language processing (NLP) offer greatopportunities to improve automatic content analysis in communication science,especially for the coding of complex semantic categories in large datasets viasupervised machine learning. However, three characteristics so far impeded thewidespread adoption of the methods in the applying disciplines: the dominanceof English language models in NLP research, the necessary computing resources,and the effort required to produce training data to fine-tune PLMs. In thisstudy, we address these challenges by using a multilingual transformer model incombination with the adapter extension to transformers, and few-shot learningmethods. We test our approach on a realistic use case from communicationscience to automatically detect claims and arguments together with their stancein the German news debate on arms deliveries to Ukraine. In three experiments,we evaluate (1) data preprocessing strategies and model variants for this task,(2) the performance of different few-shot learning methods, and (3) how wellthe best setup performs on varying training set sizes in terms of validity,reliability, replicability and reproducibility of the results. We find that ourproposed combination of transformer adapters with pattern exploiting trainingprovides a parameter-efficient and easily shareable alternative to fullyfine-tuning PLMs. It performs on par in terms of validity, while overall,provides better properties for application in communication studies. Theresults also show that pre-fine-tuning for a task on a near-domain datasetleads to substantial improvement, in particular in the few-shot setting.Further, the results indicate that it is useful to bias the dataset away fromthe viewpoints of specific prominent individuals.

Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine