Department of Electronics and Information Systems, IDLab, Ghent University, Ghent, Belgium.
Department of Computer Science and Engineering, University of Ioannina, Ioannina, Greece.
PLoS One. 2021 Sep 1;16(9):e0256922. doi: 10.1371/journal.pone.0256922. eCollection 2021.
The democratization of AI tools for content generation, combined with unrestricted access to mass media for all (e.g. through microblogging and social media), makes it increasingly hard for people to distinguish fact from fiction. This raises the question of how individual opinions evolve in such a networked environment without grounding in a known reality. The dominant approach to studying this problem uses simple models from the social sciences on how individuals change their opinions when exposed to their social neighborhood, and applies them on large social networks. We propose a novel model that incorporates two known social phenomena: (i) Biased Assimilation: the tendency of individuals to adopt other opinions if they are similar to their own; (ii) Backfire Effect: the fact that an opposite opinion may further entrench people in their stances, making their opinions more extreme instead of moderating them. To the best of our knowledge, this is the first DeGroot-type opinion formation model that captures the Backfire Effect. A thorough theoretical and empirical analysis of the proposed model reveals intuitive conditions for polarization and consensus to exist, as well as the properties of the resulting opinions.
人工智能工具的民主化使得内容生成变得更加容易，再加上所有人都可以不受限制地访问大众媒体（例如通过微博和社交媒体），人们越来越难以区分事实和虚构。这就提出了一个问题，即在没有已知现实基础的情况下，个体观点如何在这样的网络环境中演变。研究这个问题的主流方法是使用来自社会科学的简单模型，即当个人接触到他们的社交环境时，他们的观点如何发生变化，并将这些模型应用于大型社交网络上。我们提出了一个新的模型，该模型结合了两个已知的社会现象：（i）有偏差的同化：个人倾向于采纳与自己相似的其他观点；（ii）反驳效应：相反的观点可能会进一步使人们坚持自己的立场，使他们的观点更加极端，而不是缓和他们的观点。据我们所知，这是第一个捕获反驳效应的 DeGroot 型观点形成模型。对所提出模型的深入理论和实证分析揭示了存在极化和共识的直观条件，以及由此产生的观点的性质。
