Year: 2018
Type: conference-paper
Abstract: With the uncontrolled increasing of fake news and rumors over the Web, different approaches have been proposed to address the problem.In this paper, we present an approach that combines lexical, word embeddings and n-gram features to detect the stance in fake news.Our approach has been tested on the Fake News Challenge (FNC-1) dataset.Given a news title-article pair, the FNC-1 task aims at determining the relevance of the article and the title.Our proposed approach has achieved an accurate result (59.6 % Macro F1) that is close to the state-of-the-art result with 0.013 difference using a simple feature representation.Furthermore, we have investigated the importance of different lexicons in the detection of the classification labels.
DOI: 10.18653/v1/w18-5510
Authors: Bilal Ghanem, Paolo Rosso, Francisco Rangel
Institution: Universitat Politècnica de València
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W2955303243