# Fake News Detection as Natural Language Inference

arXiv: https://arxiv.org/abs/1907.07347 · HTML: https://mlsanity.com/paper/1907.07347/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2019.

## Built on by (most cited first, 9 of 9)

- [Misinfo Reaction Frames: Reasoning about Readers' Reactions to News Headlines](https://mlsanity.com/paper/2104.08790/) (2021)
- [Lessons from the Use of Natural Language Inference (NLI) in Requirements Engineering Tasks](https://mlsanity.com/paper/2405.05135/) (2024)
- [Multimodal Clickbait Detection by De-confounding Biases Using Causal Representation Inference](https://mlsanity.com/paper/2410.07673/) (2024)
- [Clickbait Detection via Large Language Models](https://mlsanity.com/paper/2306.09597/) (2023)
- [No Strong Feelings One Way or Another: Re-operationalizing Neutrality in Natural Language Inference](https://mlsanity.com/paper/2306.09918/) (2023)
- [Integrating Human-in-the-loop into Swarm Learning for Decentralized Fake News Detection](https://mlsanity.com/paper/2201.02048/) (2022)
- [Tackling Fake News in Bengali: Unraveling the Impact of Summarization vs. Augmentation on Pre-trained Language Models](https://mlsanity.com/paper/2307.06979/) (2023)
- [Arabic Fake News Detection Based on Deep Contextualized Embedding Models](https://mlsanity.com/paper/2205.03114/) (2022)
- [SOK: Fake News Outbreak 2021: Can We Stop the Viral Spread?](https://mlsanity.com/paper/2105.10671/) (2021)

## Abstract (arXiv)

This report describes the entry by the Intelligent Knowledge Management (IKM) Lab in the WSDM 2019 Fake News Classification challenge. We treat the task as natural language inference (NLI). We individually train a number of the strongest NLI models as well as BERT. We ensemble these results and retrain with noisy labels in two stages. We analyze transitivity relations in the train and test sets and determine a set of test cases that can be reliably classified on this basis. The remainder of test cases are classified by our ensemble. Our entry achieves test set accuracy of 88.063% for 3rd place in the competition.