Users’ polarization and confirmation bias play a key role in misinformation spreading on online social media. Our aim is to use this information to determine in advance potential targets for hoaxes and fake news. In this article, we introduce a framework for promptly identifying polarizing content on social media and, thus, “predicting” future fake news topics. We validate the performances of the proposed methodology on a massive Italian Facebook dataset, showing that we are able to identify topics that are susceptible to misinformation with 77% accuracy. Moreover, such information may be embedded as a new feature in an additional classifier able to recognize fake news with 91% accuracy. The novelty of our approach consists in taking into account a series of characteristics related to users’ behavior on online social media such as Facebook, making a first, important step towards the mitigation of misinformation phenomena by supporting the identification of potential misinformation targets and thus the design of tailored counter-narratives.

中文翻译：

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两极分化和假新闻

用户的两极分化和确认偏见在错误信息在在线社交媒体上的传播中发挥着关键作用。我们的目标是利用这些信息提前确定恶作剧和假新闻的潜在目标。在本文中，我们介绍了一个框架，用于快速识别社交媒体上的两极分化内容，从而“预测”未来的假新闻主题。我们在一个庞大的意大利 Facebook 数据集上验证了该方法的性能，表明我们能够以 77% 的准确率识别容易受到错误信息影响的主题。此外，这些信息可以作为新特征嵌入到能够以 91% 的准确率识别假新闻的附加分类器中。我们方法的新颖之处在于考虑了与用户在 Facebook 等在线社交媒体上的行为相关的一系列特征，