A simple but tough-to-beat baseline for the Fake News Challenge stance detection task

TL;DR

This paper introduces a simple yet highly effective baseline system for stance detection in fake news, achieving competitive results in the Fake News Challenge despite its straightforward approach.

Contribution

The paper presents a simple, robust stance detection method that rivals complex ensemble models, establishing a strong baseline for future fake news stance detection research.

Findings

Achieved third place in Fake News Challenge Stage 1

System performs competitively with complex ensemble models

Proposed a simple yet effective stance detection baseline

Abstract

Identifying public misinformation is a complicated and challenging task. An important part of checking the veracity of a specific claim is to evaluate the stance different news sources take towards the assertion. Automatic stance evaluation, i.e. stance detection, would arguably facilitate the process of fact checking. In this paper, we present our stance detection system which claimed third place in Stage 1 of the Fake News Challenge. Despite our straightforward approach, our system performs at a competitive level with the complex ensembles of the top two winning teams. We therefore propose our system as the 'simple but tough-to-beat baseline' for the Fake News Challenge stance detection task.