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Authors: Razan Masood and Ahmet Aker

Affiliation: Department of Information Engineering, University of Duisburg-Essen, Duisburg and Germany

ISBN: 978-989-758-330-8

Keyword(s): Fake News, Stance Detection, Traditional Machine Learning, Feature Engineering.

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 m ainly use deep learning models. (More)

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Paper citation in several formats:
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 - Volume 3: KMIS, ISBN 978-989-758-330-8, pages 128-135. DOI: 10.5220/0006898801280135

@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 - Volume 3: KMIS,},
year={2018},
pages={128-135},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006898801280135},
isbn={978-989-758-330-8},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - 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

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