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Text articles with false claims, especially news, have recently becomeaggravating for the Internet users. These articles are in wide circulation andreaders face difficulty discerning fact from fiction. Previous work oncredibility assessment has focused on factual analysis and linguistic features.The task's main challenge is the distinction between the features of true andfalse articles. In this paper, we propose a novel approach called CredibilityOutcome (CREDO) which aims at scoring the credibility of an article in an opendomain setting.  CREDO consists of different modules for capturing various featuresresponsible for the credibility of an article. These features includescredibility of the article's source and author, semantic similarity between thearticle and related credible articles retrieved from a knowledge base, andsentiments conveyed by the article. A neural network architecture learns thecontribution of each of these modules to the overall credibility of an article.Experiments on Snopes dataset reveals that CREDO outperforms thestate-of-the-art approaches based on linguistic features.

Neural Network Architecture for Credibility Assessment of Textual Claims