We test whether financial LLM agents respond differently to reports with identical facts, forecasts and recommendations but different wording. The reports differ only in a closing sentence stating the sender’s affect. The most consistent finding is linguistic: high-arousal wording increases readers’ word-based arousal scores on average and also changes teammates’ language. The response varies across rolespecific task instructions, although its magnitude and ordering change across prompt paraphrases, scoring methods, word-removal controls and model families. The central role comparison contains 7,080 generations, but all concern one underlying deposit-outflow event; the 59 matched pairs are seeded realizations of the same financial payload rather than independent financial situations. Separately varying alarm and urgency shows that both raise word-based arousal, while alarmed wording adds an apprehension component detected by an emotion classifier. Behavioral effects are less consistent: in the primary model, alarmed wording reduces adherence to BUY and SELL recommendations, but the SELL reported-size effect is not detected in two additional model families. A filter that removes explicit alarm while preserving checked financial fields leaves most of the linguistic response intact.

The rapid flow of information and the abundance of text data on the Internet have brought about the urgent demand for the construction of monitoring resources and techniques used for various purposes. To extract facets of information useful for particular domains from such large and dynamically growing corpora requires an unsupervised yet transparent ways of analyzing the textual data. This paper proposed a hybrid collocation analysis as a potential method to retrieve and summarize Taiwan-related topics posted on Weibo and PTT. By grouping collocates of 臺灣 ‘Taiwan’ into clusters of topics via either word embeddings clustering or Latent Dirichlet allocation, lists of collocates can be converted to probability distributions such that distances and similarities can be defined and computed. With this method, we conduct a diachronic analysis of the similarity between Weibo and PTT, providing a way to pinpoint when and how the topic similarity between the two rises or falls. A fine-grained view on the grammatical behavior and political implications is attempted, too. This study thus sheds light on alternative explainable routes for future social media listening method on the understanding of cross-strait relationship.