论文标题
自动假新闻检测：当前的模型是“事实检查”或“肠道检查”？
Automatic Fake News Detection: Are current models "fact-checking" or "gut-checking"?
论文作者
论文摘要
自动假新闻检测模型表面上是基于逻辑的，可以通过支持或驳斥所得的Web查询中发现的证据来确定标题中提出的索赔的真相。据信这些模型以某种方式是推理的。但是，已经表明，在不考虑索赔的情况下，可以实现这些相同的结果，或者更好的结果，只有证据。这意味着其他信号包含在检查的证据中，并且可能基于可操作的因素，例如情感，情感或词性或部分词性（POS）频率，这些因素容易受到对抗性输入的影响。我们通过多种形式的神经和非神经预处理和样式转移来中和其中一些信号，并发现无关指标的这种扁平化可以诱导该模型实际需要索赔和证据表现良好。我们最终是在构建模型的结论下，使用情感向量建立在词典中，并通过“情感关注”机制来适当地加重某些情绪。我们提供可量化的结果，证明我们的假设是可操作特征用于事实检查。
Automatic fake news detection models are ostensibly based on logic, where the truth of a claim made in a headline can be determined by supporting or refuting evidence found in a resulting web query. These models are believed to be reasoning in some way; however, it has been shown that these same results, or better, can be achieved without considering the claim at all -- only the evidence. This implies that other signals are contained within the examined evidence, and could be based on manipulable factors such as emotion, sentiment, or part-of-speech (POS) frequencies, which are vulnerable to adversarial inputs. We neutralize some of these signals through multiple forms of both neural and non-neural pre-processing and style transfer, and find that this flattening of extraneous indicators can induce the models to actually require both claims and evidence to perform well. We conclude with the construction of a model using emotion vectors built off a lexicon and passed through an "emotional attention" mechanism to appropriately weight certain emotions. We provide quantifiable results that prove our hypothesis that manipulable features are being used for fact-checking.
