Publications

We present the Social Influence Technique Taxonomy (SITT), a comprehensive framework of 58 empirically grounded techniques organized into nine categories, designed to detect subtle forms of social influence in textual content. We investigate LLMs’ ability to identify various forms of social influence by constructing the SITT dataset – a 746-dialogue corpus annotated by 11 experts in Polish and translated into English. Using hierarchical multi-label classification, we benchmark five LLMs including GPT-4o, Claude 3.5, Llama-3.1, Mixtral, and PLLuM. Our results show that while some models achieved moderate success (Claude 3.5: F1 = 0.45 for categories), overall performance remains limited, particularly for context-sensitive techniques.