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

arXiv: https://arxiv.org/abs/1707.03264 · HTML: https://mlsanity.com/paper/1707.03264/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2017.

## Built on by (most cited first, 26 of 26)

- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://mlsanity.com/paper/2103.00242/) (2021)
- [Logical Fallacy Detection](https://mlsanity.com/paper/2202.13758/) (2022)
- [Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims](https://mlsanity.com/paper/2204.12294/) (2022)
- [Is Stance Detection Topic-Independent and Cross-topic Generalizable? -- A Reproduction Study](https://mlsanity.com/paper/2110.07693/) (2021)
- [Stanceosaurus: Classifying Stance Towards Multilingual Misinformation](https://mlsanity.com/paper/2210.15954/) (2022)
- [Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks](https://mlsanity.com/paper/2212.12621/) (2022)
- [A transformer based approach for fighting COVID-19 fake news](https://mlsanity.com/paper/2101.12027/) (2021)
- [Data Augmentation for Biomedical Factoid Question Answering](https://mlsanity.com/paper/2204.04711/) (2022)
- [TIB's Visual Analytics Group at MediaEval '20: Detecting Fake News on Corona Virus and 5G Conspiracy](https://mlsanity.com/paper/2101.03529/) (2021)
- [ConsistTL: Modeling Consistency in Transfer Learning for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2212.04262/) (2022)
- [WhatTheWikiFact: Fact-Checking Claims Against Wikipedia](https://mlsanity.com/paper/2105.00826/) (2021)
- [Hostility Detection and Covid-19 Fake News Detection in Social Media](https://mlsanity.com/paper/2101.05953/) (2021)
- [Back to the Basics: A Quantitative Analysis of Statistical and Graph-Based Term Weighting Schemes for Keyword Extraction](https://mlsanity.com/paper/2104.08028/) (2021)
- [Tribrid: Stance Classification with Neural Inconsistency Detection](https://mlsanity.com/paper/2109.06508/) (2021)
- [Efficiency and Effectiveness of LLM-Based Summarization of Evidence in Crowdsourced Fact-Checking](https://mlsanity.com/paper/2501.18265/) (2025)
- [Incongruity Detection between Bangla News Headline and Body Content through Graph Neural Network](https://mlsanity.com/paper/2211.07709/) (2022)
- [SocialPET: Socially Informed Pattern Exploiting Training for Few-Shot Stance Detection in Social Media](https://mlsanity.com/paper/2403.05216/) (2024)
- [Reliable News or Propagandist News? A Neurosymbolic Model Using Genre, Topic, and Persuasion Techniques to Improve Robustness in Classification](https://mlsanity.com/paper/2604.01936/) (2026)
- [SOK: Fake News Outbreak 2021: Can We Stop the Viral Spread?](https://mlsanity.com/paper/2105.10671/) (2021)
- [Leveraging Commonsense Knowledge on Classifying False News and Determining Checkworthiness of Claims](https://mlsanity.com/paper/2108.03731/) (2021)
- [UofA-Truth at Factify 2022 : Transformer And Transfer Learning Based Multi-Modal Fact-Checking](https://mlsanity.com/paper/2203.07990/) (2022)
- [BaIT: Barometer for Information Trustworthiness](https://mlsanity.com/paper/2206.07535/) (2022)
- [Combination Of Convolution Neural Networks And Deep Neural Networks For Fake News Detection](https://mlsanity.com/paper/2210.08331/) (2022)
- [TeClass: A Human-Annotated Relevance-based Headline Classification and Generation Dataset for Telugu](https://mlsanity.com/paper/2404.11349/) (2024)
- [Sequential Classification of Misinformation](https://mlsanity.com/paper/2409.04860/) (2024)
- [BERT-based model for Vietnamese Fact Verification Dataset](https://mlsanity.com/paper/2503.00356/) (2025)

## Abstract (arXiv)

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.