Neural Network Architecture for Credibility Assessment of Textual Claims
This addresses the challenge of discerning fact from fiction for Internet users, though it appears incremental by combining existing feature types with a neural network.
The paper tackles the problem of assessing the credibility of textual claims, especially in news articles, by proposing a neural network architecture called CREDO that integrates multiple features like source credibility and semantic similarity, and it outperforms state-of-the-art linguistic feature-based approaches on the Snopes dataset.
Text articles with false claims, especially news, have recently become aggravating for the Internet users. These articles are in wide circulation and readers face difficulty discerning fact from fiction. Previous work on credibility assessment has focused on factual analysis and linguistic features. The task's main challenge is the distinction between the features of true and false articles. In this paper, we propose a novel approach called Credibility Outcome (CREDO) which aims at scoring the credibility of an article in an open domain setting. CREDO consists of different modules for capturing various features responsible for the credibility of an article. These features includes credibility of the article's source and author, semantic similarity between the article and related credible articles retrieved from a knowledge base, and sentiments conveyed by the article. A neural network architecture learns the contribution of each of these modules to the overall credibility of an article. Experiments on Snopes dataset reveals that CREDO outperforms the state-of-the-art approaches based on linguistic features.