We propose a novel approach framed in terms of information theory and entropy to tackle the issue of the propagation of conspiracy theories. We represent the initial report of an event (such as the 9/11 terroristic attack) as a series of strings of information, each string classified by a two-state variable Ei = ±1, i = 1, …, N. If the values of the Ei are set to −1 for all strings, a state of minimum entropy is achieved. Comments on the report, focusing repeatedly on several strings Ek, might alternate their meaning (from −1 to +1). The representation of the event is turned fuzzy with an increased entropy value. Beyond some threshold value of entropy, chosen by simplicity to its maximum value, meaning N/2 variables with Ei = 1, the chance is created that a conspiracy theory might be initiated/propagated. Therefore, the evolution of the associated entropy is a way to measure the degree of penetration of a conspiracy theory. Our general framework relies on online content made voluntarily available by crowds of people, in response to some news or blog articles published by official news agencies. We apply different aggregation levels (comment, person, discussion thread) and discuss the associated patterns of entropy change.
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机译：我们提出了一种以信息论和熵为框架的新颖方法来解决阴谋论传播的问题。我们将事件（如9/11恐怖袭击）的初始报告表示为一系列信息串，每个信息串均由两个状态变量E i =±1，i = 1，…，N。如果将所有字符串的E i 的值设置为-1，则将达到最小熵的状态。对报告的评论（反复关注几个字符串E k ）可能会改变其含义（从-1到+1）。事件的表示随着熵值的增加而变得模糊。超出某个熵的阈值（通过简单地选择其最大值），这意味着N / 2个变量的E i = 1，这可能会引发一个阴谋论可能被发起/传播。因此，相关熵的演化是一种衡量阴谋论渗透程度的方法。我们的总体框架依赖于人群的自愿提供的在线内容，以响应官方新闻机构发布的一些新闻或博客文章。我们应用了不同的聚合级别（评论，人员，讨论线程），并讨论了熵变化的相关模式。
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