activity
20242026
collaborators

8 papers

cs.CL2026

Early Language Learning via Spreading Activation and Category Exploration in Complex Networks

Salvatore Citraro

Is word acquisition in children uneven with respect to semantic and lexical categories? To answer this question, we model early language learning as a search on a graph-based menta…

cs.AI2026

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…

cs.IR2025

A survey on the impacts of recommender systems on users, items, and human-AI ecosystems

Luca Pappalardo, Salvatore Citraro, Giuliano Cornacchia +12

Recommendation systems and assistants (in short, recommenders) influence through online platforms most actions of our daily lives, suggesting items or providing solutions based on…

cs.CL2025

SpreadPy: A Python tool for modelling spreading activation and superdiffusion in cognitive multiplex networks

Salvatore Citraro, Edith Haim, Alessandra Carini +3

We introduce SpreadPy as a Python library for simulating spreading activation in cognitive single-layer and multiplex networks. Our tool is designed to perform numerical simulation…

cs.SI2025

Online posting effects: Unveiling the non-linear journeys of users in depression communities on Reddit

Virginia Morini, Salvatore Citraro, Elena Sajno +4

Social media platforms have become pivotal as self-help forums, enabling individuals to share personal experiences and seek support. However, on topics as sensitive as depression,…

cs.AI2024

Forma mentis networks predict creativity ratings of short texts via interpretable artificial intelligence in human and GPT-simulated raters

Edith Haim, Natalie Fischer, Salvatore Citraro +2

Creativity is a fundamental skill of human cognition. We use textual forma mentis networks (TFMN) to extract network (semantic/syntactic associations) and emotional features from a…