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Identifying the veracity of a news article is an interesting problem whileautomating this process can be a challenging task. Detection of a news articleas fake is still an open question as it is contingent on many factors which thecurrent state-of-the-art models fail to incorporate. In this paper, we explorea subtask to fake news identification, and that is stance detection. Given anews article, the task is to determine the relevance of the body and its claim.We present a novel idea that combines the neural, statistical and externalfeatures to provide an efficient solution to this problem. We compute theneural embedding from the deep recurrent model, statistical features from theweighted n-gram bag-of-words model and handcrafted external features with thehelp of feature engineering heuristics. Finally, using deep neural layer allthe features are combined, thereby classifying the headline-body news pair asagree, disagree, discuss, or unrelated. We compare our proposed technique withthe current state-of-the-art models on the fake news challenge dataset. Throughextensive experiments, we find that the proposed model outperforms all thestate-of-the-art techniques including the submissions to the fake newschallenge.

On the Benefit of Combining Neural, Statistical and External Features for Fake News Identification