# Automatic Stance Detection Using End-to-End Memory Networks

arXiv: https://arxiv.org/abs/1804.07581 · HTML: https://mlsanity.com/paper/1804.07581/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2018.

## Built on by (most cited first, 14 of 14)

- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://mlsanity.com/paper/2103.00242/) (2021)
- [Open-Domain, Content-based, Multi-modal Fact-checking of Out-of-Context Images via Online Resources](https://mlsanity.com/paper/2112.00061/) (2021)
- [Cross-Domain Label-Adaptive Stance Detection](https://mlsanity.com/paper/2104.07467/) (2021)
- [Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training](https://mlsanity.com/paper/2109.06050/) (2021)
- [AraStance: A Multi-Country and Multi-Domain Dataset of Arabic Stance Detection for Fact Checking](https://mlsanity.com/paper/2104.13559/) (2021)
- [Knowledge Grounded Conversational Symptom Detection with Graph Memory Networks](https://mlsanity.com/paper/2101.09773/) (2021)
- [Stance Detection with BERT Embeddings for Credibility Analysis of Information on Social Media](https://mlsanity.com/paper/2105.10272/) (2021)
- [Who Blames or Endorses Whom? Entity-to-Entity Directed Sentiment Extraction in News Text](https://mlsanity.com/paper/2106.01033/) (2021)
- [TATA: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings](https://mlsanity.com/paper/2310.14450/) (2023)
- [Tribrid: Stance Classification with Neural Inconsistency Detection](https://mlsanity.com/paper/2109.06508/) (2021)
- [Examining Political Rhetoric with Epistemic Stance Detection](https://mlsanity.com/paper/2212.14486/) (2022)
- [SocialPET: Socially Informed Pattern Exploiting Training for Few-Shot Stance Detection in Social Media](https://mlsanity.com/paper/2403.05216/) (2024)
- [Rating Facts under Coarse-to-fine Regimes](https://mlsanity.com/paper/2107.06051/) (2021)
- [Combination Of Convolution Neural Networks And Deep Neural Networks For Fake News Detection](https://mlsanity.com/paper/2210.08331/) (2022)

## Abstract (arXiv)

We present a novel end-to-end memory network for stance detection, which jointly (i) predicts whether a document agrees, disagrees, discusses or is unrelated with respect to a given target claim, and also (ii) extracts snippets of evidence for that prediction. The network operates at the paragraph level and integrates convolutional and recurrent neural networks, as well as a similarity matrix as part of the overall architecture. The experimental evaluation on the Fake News Challenge dataset shows state-of-the-art performance.