Linguistic Features Predict the Truthfulness of Short Political Statements

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.