With social media, the flow of uncertified information is constantly increasing, with the risk that more people will trust low-credible information sources. To design effective strategies against this phenomenon, it is of paramount importance to understand how people end up believing one source rather than another. To this end, we propose a realistic and cognitively affordable heuristic mechanism for opinion formation inspired by the well-known belief propagation algorithm. In our model, an individual observing a network of information sources must infer which of them are reliable and which are not. We study how the individual's ability to identify credible sources, and hence to form correct opinions, is affected by the noise in the system, intended as the amount of disorder in the relationships between the information sources in the network. We find numerically and analytically that there is a critical noise level above which it is impossible for the individual to detect the nature of the sources. Moreover, by comparing our opinion formation model with existing ones in the literature, we show under what conditions people's opinions can be reliable. Overall, our findings imply that the increasing complexity of the information environment is a catalyst for misinformation channels.

翻译：随着社交媒体的发展，未经认证的信息流持续增长，导致更多人面临信任低可信信息源的风险。为设计针对这一现象的有效策略，理解人们最终相信某个信息源而非其他信息源的内在机制至关重要。为此，我们提出了一种基于著名置信传播算法的现实且认知上可行的启发式观点形成机制。在该模型中，观察信息源网络的个体需要推断其中哪些信息源可信、哪些不可信。我们研究了个体识别可信信息源（进而形成正确观点）的能力如何受系统噪声影响——该噪声被定义为网络中信息源之间关系的无序程度。通过数值分析与理论推导发现，存在一个临界噪声阈值：当噪声超过该阈值时，个体将完全无法识别信息源的性质。此外，通过与文献中现有观点形成模型进行比较，我们揭示了在何种条件下人们的观点能够保持可靠。总体而言，我们的研究表明，信息环境复杂性的不断加剧是错误信息传播的催化剂。