巴西和美国不可靠新闻分类探析
An Exploration of Unreliable News Classification in Brazil and The U.S
摘要：
在世界许多地方，不可靠信息的传播正在增加。信息的迅速传播和互联网赋予的匿名性促进了这种扩张。不可靠信息的传播是一个得到充分研究的问题，它与负面的社会影响有关。在以前的工作中，我们已经确定了美国媒体中可靠和不可靠来源的新闻文章结构的显着差异。我们这项工作的目标是探索巴西媒体的这种差异。我们在两个数据集中发现了显著的特征：一个是葡萄牙语的巴西新闻，另一个是英语的美国新闻。我们的研究结果表明，相关的写作风格的功能是突出的，在这两个数据集，尽管语言的差异，一些功能具有普遍的行为，是显着的美国和巴西的新闻文章。最后，我们结合这两个数据集，并使用通用特征来构建一个机器学习分类器，以预测新闻文章的来源类型是可靠还是不可靠。
英文摘要：
The propagation of unreliable information is on the rise in many places around the world. This expansion is facilitated by the rapid spread of information and anonymity granted by the Internet. The spread of unreliable information is a wellstudied issue and it is associated with negative social impacts. In a previous work, we have identified significant differences in the structure of news articles from reliable and unreliable sources in the US media. Our goal in this work was to explore such differences in the Brazilian media. We found significant features in two data sets: one with Brazilian news in Portuguese and another one with US news in English. Our results show that features related to the writing style were prominent in both data sets and, despite the language difference, some features have a universal behavior, being significant to both US and Brazilian news articles. Finally, we combined both data sets and used the universal features to build a machine learning classifier to predict the source type of a news article as reliable or unreliable.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
