Related papers: From Perils to Possibilities: Understanding how Human (and AI) Biases affect Online Fora

From Perils to Possibilities: Understanding how Human (and AI) Biases affect Online Fora

URL: http://arxiv.org/abs/2403.14298v1
Date: Thu, 21 Mar 2024 11:04:41 GMT
Title: From Perils to Possibilities: Understanding how Human (and AI) Biases affect Online Fora
Authors: Virginia Morini, Valentina Pansanella, Katherine Abramski, Erica Cau, Andrea Failla, Salvatore Citraro, Giulio Rossetti,
Abstract summary: Review explores the dynamics of social interactions, user-generated contents, and biases within the context of social media analysis. Three key points of view are: online debates, online support, and human-AI interactions.
Score: 0.12564343689544843
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Social media platforms are online fora where users engage in discussions, share content, and build connections. This review explores the dynamics of social interactions, user-generated contents, and biases within the context of social media analysis (analyzing works that use the tools offered by complex network analysis and natural language processing) through the lens of three key points of view: online debates, online support, and human-AI interactions. On the one hand, we delineate the phenomenon of online debates, where polarization, misinformation, and echo chamber formation often proliferate, driven by algorithmic biases and extreme mechanisms of homophily. On the other hand, we explore the emergence of online support groups through users' self-disclosure and social support mechanisms. Online debates and support mechanisms present a duality of both perils and possibilities within social media; perils of segregated communities and polarized debates, and possibilities of empathy narratives and self-help groups. This dichotomy also extends to a third perspective: users' reliance on AI-generated content, such as the ones produced by Large Language Models, which can manifest both human biases hidden in training sets and non-human biases that emerge from their artificial neural architectures. Analyzing interdisciplinary approaches, we aim to deepen the understanding of the complex interplay between social interactions, user-generated content, and biases within the realm of social media ecosystems.

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