论文标题
狂热：使用能量流的假新闻检测系统
FaNDS: Fake News Detection System Using Energy Flow
论文作者
论文摘要
最近，“假新闻”一词被广泛而广泛地用于虚假，错误信息，骗局，宣传，讽刺，讽刺，谣言，点击诱饵和垃圾新闻。它已成为世界范围内的一个严重问题。我们提出了一个新的系统Fands，该系统有效地检测到了假新闻。该系统基于某些以前的作品中使用的几个概念，但在不同的上下文中。有两个主要概念：不一致的图和能量流。不一致的图包含新闻项目，作为节点和边缘之间的意见不一致。能量流将每个节点分配一个初始能量，然后沿边缘传播一些能量，直到所有节点上的能量分布收敛。为了说明狂热，我们使用了《假新闻挑战》（FNC-1）中的原始数据。首先，必须重建数据才能生成不一致图。该图包含各种子图，上面有明确的形状，代表新闻项目之间的不同类型的连接。然后应用能量流量方法。高能量的节点是假新闻的候选人。在我们的实验中，所有这些确实是假新闻，因为我们使用多个可靠的网站检查了每个新闻。我们比较了其他几种假新闻检测方法，发现发现假新闻项目更敏感。
Recently, the term "fake news" has been broadly and extensively utilized for disinformation, misinformation, hoaxes, propaganda, satire, rumors, click-bait, and junk news. It has become a serious problem around the world. We present a new system, FaNDS, that detects fake news efficiently. The system is based on several concepts used in some previous works but in a different context. There are two main concepts: an Inconsistency Graph and Energy Flow. The Inconsistency Graph contains news items as nodes and inconsistent opinions between them for edges. Energy Flow assigns each node an initial energy and then some energy is propagated along the edges until the energy distribution on all nodes converges. To illustrate FaNDS we use the original data from the Fake News Challenge (FNC-1). First, the data has to be reconstructed in order to generate the Inconsistency Graph. The graph contains various subgraphs with well-defined shapes that represent different types of connections between the news items. Then the Energy Flow method is applied. The nodes with high energy are the candidates for being fake news. In our experiments, all these were indeed fake news as we checked each using several reliable web sites. We compared FaNDS to several other fake news detection methods and found it to be more sensitive in discovering fake news items.
