The problem of automatic Fake News detection in digital media of news distribution (DMND - e.g., social networks, online newspaper, etc) has become even more relevant. Among the main detection approaches, the one based on crowd signals from DMND users has stood out by obtaining promising results. In essence, in order to classify a piece of news as fake or not fake, such approach explores the collective sense by combining opinions (signals, i.e., votes about the classification of some news) of a high number of users (crowd), considering the reputations of these users regarding their capacity of identifying Fake News. Although promising, the Crowd Signals approach has a significant limitation: it depends on the explicit user opinion (which is not always available) about the classification of the analyzed news. Such unavailability may be caused by the absence of a functionality in the DMND that collects user opinion about the news, or by the simple option of the users in not giving their opinion. Facing this limitation, the present work raises the hypothesis that it is possible to build models of Fake News detection with a performance comparable to the Crowd Signals based approach, avoiding the dependence on the explicit opinion of DMND users. To validate this hypothesis, the present work proposes HCS, an approach based on crowd signals that considers implicit user opinions instead of the explicit ones. The implicit opinions are inferred from the behavior of users concerning the dissemination of the news analyzed. Inspired in Meta-Learning, the HCS can also use the explicit opinions from machines (news classification models) to complement the implicit user opinions by means of hybrid Crowds. Experiments carried out in five datasets presented significant evidence that confirms the raised hypothesis. Even without considering DMND users’ explicit opinions, HCS was able to achieve results comparable to the ones produced by the Crowd Signals approach. Besides that, the results also revealed a performance improvement of HCS when the implicit opinions of the users were combined with the explicit opinions of machines.

在新闻分发的数字媒体（DMND - 例如社交网络、在线报纸等）中自动检测假新闻的问题变得更加重要。在主要的检测方法中，基于来自 DMND 用户的人群信号的一种已经获得了可喜的结果。本质上，为了将一条新闻归类为假新闻或非假新闻，这种方法通过结合大量用户（人群）的意见（信号，即对某些新闻的分类进行投票）来探索集体意识，考虑这些用户在识别假新闻的能力方面的声誉。尽管很有前景，但人群信号方法有一个很大的局限性：它取决于用户对所分析新闻分类的明确意见（并不总是可用的）。这种不可用可能是由于 DMND 中没有收集用户对新闻的意见的功能，或者是用户选择不发表他们的意见的简单选项。面对这一限制，目前的工作提出了一个假设，即有可能建立假新闻检测模型，其性能可与基于人群信号的方法相媲美，避免依赖于 DMND 用户的明确意见。为了验证这一假设，目前的工作提出 避免依赖 DMND 用户的明确意见。为了验证这一假设，目前的工作提出 避免依赖 DMND 用户的明确意见。为了验证这一假设，目前的工作提出HCS，一种基于人群信号的方法，它考虑隐含的用户意见而不是明确的意见。隐含意见是从用户对所分析的新闻传播的行为推断出来的。受到元学习的启发，HCS还可以使用来自机器（新闻分类模型）的明确意见，通过混合人群来补充隐含的用户意见。在五个数据集中进行的实验提供了证实提出的假设的重要证据。即使不考虑 DMND 用户的明确意见，HCS也能够实现与人群信号方法产生的结果相当的结果。除此之外，结果还揭示了HCS的性能提升 当用户的隐含意见与机器的显性意见相结合时。