4 papers
The TEA Nets framework combines AI and cognitive network science to model targets, events and actors in text
Sebastiano Franchini, Alexis Carrillo, Edoardo Sebastiano De Duro +3
We introduce Target-Event-Agent Networks (TEA Nets) as a computational framework to extract subjects (``Agents"), verbs (``Events"), and objects (``Targets") from texts. Grounded i…
Mapping how LLMs debate societal issues when shadowing human personality traits, sociodemographics and social media behavior
Ali Aghazadeh Ardebili, Massimo Stella
Large Language Models (LLMs) can strongly shape social discourse, yet datasets investigating how LLM outputs vary across controlled social and contextual prompting remain sparse. C…
LLMs can persuade only psychologically susceptible humans on societal issues, via trust in AI and emotional appeals, amid logical fallacies
Alexis Carrillo, Salvatore Citraro, Ali Aghazhadeh Ardebili +5
Scarce longitudinal evidence examines LLMs' persuasiveness and humanness along time-evolving psychological frameworks. We introduce Talk2AI, a longitudinal framework quantifying ps…
Talk2AI: A Longitudinal Dataset of Human--AI Persuasive Conversations
Alexis Carrillo, Enrique Taietta, Ali Aghazadeh Ardebili +2
Talk2AI is a large-scale longitudinal dataset of 3,080 conversations (totaling 30,800 turns) between human participants and Large Language Models (LLMs), designed to support resear…