The Fake News Challenge: Stance Detection using Traditional Machine Learning Approaches

Razan Masood, Ahmet Aker

2018

Abstract

Fake news has caused sensation lately, and this term is the Collins Dictionary Word of the Year 2017. As the news are disseminated very fast in the era of social networks, an automated fact checking tool becomes a requirement. However, a fully automated tool that judges a claim to be true or false is always limited in functionality, accuracy and understandability. Thus, an alternative suggestion is to collaborate a number of analysis tools in one platform which help human fact checkers and normal users produce better judging based on many aspects. A stance detection tool is a first stage of an online challenge that aims to detect fake news. The goal is to determine the relative perspective of a news article towards its title. In this paper, we tackle the challenge of stance detection by utilizing traditional machine learning algorithms along with problem specific feature engineering. Our results show that these models outperform the best outcomes of the participating solutions which mainly use deep learning models.

Paper Citation

in Harvard Style

Masood R. and Aker A. (2018). The Fake News Challenge: Stance Detection using Traditional Machine Learning Approaches. In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - Volume 3: KMIS; ISBN 978-989-758-330-8, SciTePress, pages 128-135. DOI: 10.5220/0006898801280135

in Bibtex Style

@conference{kmis18,
author={Razan Masood and Ahmet Aker},
title={The Fake News Challenge: Stance Detection using Traditional Machine Learning Approaches},
booktitle={Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - Volume 3: KMIS},
year={2018},
pages={128-135},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006898801280135},
isbn={978-989-758-330-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - Volume 3: KMIS
TI - The Fake News Challenge: Stance Detection using Traditional Machine Learning Approaches
SN - 978-989-758-330-8
AU - Masood R.
AU - Aker A.
PY - 2018
SP - 128
EP - 135
DO - 10.5220/0006898801280135
PB - SciTePress