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Disentangling individual differences in cognitive response mechanisms for rating scale items: A flexible-mixture multidimensional IRTree approach
No full textExercise and eating motivation in weight loss maintenance: revisiting the motivational spillover with the NoHoW study
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Cultural distance through the lens of social closeness and individual experiences with diversity : Unveiling attitudes toward recent immigrants in Germany using a factorial survey experiment
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LLM4DDC: Adopting Large Language Models for research data classification using Dewey Decimal Classification
No full textClassifying research data in institutional repositories is time-consuming and challenging. While the Dewey Decimal Classification (DDC) system is widely used in subject classification for texts, its application to research data metadata has been limited so far. This study explores the possible use of large language models (LLMs) and small language models (SLMs) for the automatic classification of research data in the context of DDC. This study uses sample data from an existing dataset compiled from different institutions mainly in Germany. We use a prompt engineering approach for LLMs, and fine tuning for SLMs, where we use RoBERTa as a baseline. Our results show that LLMs with prompt engineering currently are not able to classify metadata of research data into DDC classes as good as SLMs with fine tuning. To foster adoption, we openly release our models, code, and datasets for integration into research data infrastructures at GitHub
Small word change, large effect on news users? How the use of gender-inclusive language in news articles influences news selection and news engagement
No full textBeyond content : investors' chatter, interaction and earnings announcement returns
No full textWe study the relationship between investors’ social media activity and earnings announcement returns. To distinguish between information contained in peer-to-peer interaction and user-posted content, we analyze conversation networks on Reddit using centrality metrics from network science and classify user sentiment with large language models. We show that pre-announcement sentiment is positively associated with short-term cumulative abnormal returns only if it does not spark pre-announcement controversy. If pre-announcement controversy arises, we document a negative association. Our findings present a more nuanced view on the wisdom of crowds hypothesis, highlighting that peer-to-peer interaction on social media exhibits a pattern of normalization, and thus contains informational value beyond conten