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Linguistic Features Predict the Truthfulness of Short Political Statements

机译:语言特征预测简短的政治言论的真实性

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摘要

Checking the truth value of political statements is difficult. Fact checking computationally has therefore not been very successful. An alternative to checking the truth value of a statement is to not consider the facts that are stated, but the way the statement is expressed. Using linguistic features from seven computational linguistic algorithms, we investigated whether truth-false statements and the definitiveness with which the statement is expressed can be predicted using linguistic features. In a training set we found that both distinctiveness and truthfulness of the statement predicted linguistic variables. These variables corresponded to those mentioned in deception literature. Next, we used a new set of political statements and determined whether the same linguistic variables would be able to predict the definitiveness and truthfulness of the statement. Given the fact that the political statements are short, one-sentence statements, allowing for a large variability in linguistic variables, discriminant analyses showed that the function obtained from the training set allowed for an accurate classification of 57 - 59% of the data. These findings are encouraging, for instance for first analysis on the truth value and verifiability of political statements.
机译:检查政治言论的真实价值是困难的。因此,通过计算进行事实检查不是很成功。检查语句的真实值的一种替代方法是,不考虑陈述的事实,而是考虑语句的表达方式。使用来自七个计算语言算法的语言特征,我们研究了使用语言特征是否可以预测真假陈述和陈述的确定性。在训练集中,我们发现陈述的独特性和真实性都预示了语言变量。这些变量对应于欺骗文献中提到的变量。接下来,我们使用了一组新的政治声明,并确定相同的语言变量是否能够预测该声明的确定性和真实性。鉴于政治陈述是简短的一句话陈述,允许语言变量发生很大的变化,判别分析表明,从训练集中获得的功能可以对数据进行57-59%的准确分类。这些发现令人鼓舞,例如,对于政治陈述的真实价值和可验证性进行首次分析。

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