Stance Detection in Fake News A Combined Feature Representation

Bilal Ghanem, Paolo Rosso, Francisco Rangel


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.
Anthology ID:
W18-5510
Volume:
Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)
Month:
November
Year:
2018
Address:
Brussels, Belgium
Venues:
EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
66–71
Language:
URL:
https://aclanthology.org/W18-5510
DOI:
10.18653/v1/W18-5510
Bibkey:
Cite (ACL):
Bilal Ghanem, Paolo Rosso, and Francisco Rangel. 2018. Stance Detection in Fake News A Combined Feature Representation. In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pages 66–71, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Stance Detection in Fake News A Combined Feature Representation (Ghanem et al., 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-5510.pdf