From Perils to Possibilities: Understanding how Human (and AI) Biases affect Online Fora
arxiv(2024)
摘要
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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