The paper develops a stochastic model of drift in human beliefs that shows that today's sheer volume of accessible information, combined with consumers' confirmation bias and natural preference to more outlying content, necessarily lead to increased polarization. The model explains the paradox of growing ideological fragmentation in the age of increased sharing. As social media, search engines, and other real-time information sharing outlets purport to facilitate access to information, a need for content filtering arises due to the ensuing information overload. In general, consumers select information that matches their individual views and values. The bias inherent in such selection is echoed by today's information curation services that maximize user engagement by filtering new content in accordance with observed consumer preferences. Consequently, individuals get exposed to increasingly narrower bands of the ideology spectrum, thus fragmenting society into increasingly ideologically isolated enclaves. We call this dynamic the paradox of information access. The model also suggests the disproportionate damage attainable with a small infusion of well-positioned misinformation. The paper describes the modeling methodology, and evaluates modeling results for different population sizes and parameter settings.

中文翻译：

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信息获取的悖论：社交媒体引发的极化建模

该论文开发了一个人类信念漂移的随机模型，该模型表明，当今大量的可访问信息，加上消费者的确认偏见和对更多边远内容的自然偏好，必然会导致两极分化加剧。该模型解释了在共享增加的时代意识形态日益分裂的悖论。由于社交媒体、搜索引擎和其他实时信息共享渠道旨在促进对信息的访问，由于随之而来的信息过载，需要进行内容过滤。一般而言，消费者选择与其个人观点和价值观相匹配的信息。这种选择中固有的偏见在今天得到了回应。通过根据观察到的消费者偏好过滤新内容来最大化用户参与度的信息管理服务。因此，个人接触到的意识形态范围越来越窄，从而将社会分裂成意识形态上越来越孤立的飞地。我们称这种动态为信息访问悖论。该模型还表明，少量注入定位良好的错误信息可以获得不成比例的损害。本文描述了建模方法，并评估了不同人口规模和参数设置的建模结果。该模型还表明，少量注入定位良好的错误信息可以获得不成比例的损害。本文描述了建模方法，并评估了不同人口规模和参数设置的建模结果。该模型还表明，少量注入定位良好的错误信息可以获得不成比例的损害。本文描述了建模方法，并评估了不同人口规模和参数设置的建模结果。