Stance Detection

56 papers with code • 4 benchmarks • 18 datasets

Stance detection is the extraction of a subject's reaction to a claim made by a primary actor. It is a core part of a set of approaches to fake news assessment.


  • Source: "Apples are the most delicious fruit in existence"
  • Reply: "Obviously not, because that is a reuben from Katz's"
  • Stance: deny

Most implemented papers

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

uclmr/fakenewschallenge 11 Jul 2017

Identifying public misinformation is a complicated and challenging task.

A Retrospective Analysis of the Fake News Challenge Stance Detection Task

hanselowski/athene_system 13 Jun 2018

To date, there is no in-depth analysis paper to critically discuss FNC-1's experimental setup, reproduce the results, and draw conclusions for next-generation stance classification methods.

Combining Similarity Features and Deep Representation Learning for Stance Detection in the Context of Checking Fake News

LuisPB7/fnc-msc 2 Nov 2018

Specifically, we use bi-directional Recurrent Neural Networks, together with max-pooling over the temporal/sequential dimension and neural attention, for representing (i) the headline, (ii) the first two sentences of the news article, and (iii) the entire news article.

Stance Prediction for Russian: Data and Analysis

npenzin/rustance 5 Sep 2018

As well as presenting this openly-available dataset, the first of its kind for Russian, the paper presents a baseline for stance prediction in the language.

Detecting Incongruity Between News Headline and Body Text via a Deep Hierarchical Encoder

david-yoon/detecting-incongruity 17 Nov 2018

Some news headlines mislead readers with overrated or false information, and identifying them in advance will better assist readers in choosing proper news stories to consume.

A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking

UKPLab/conll2019-snopes-experiments CONLL 2019

Automated fact-checking based on machine learning is a promising approach to identify false information distributed on the web.

Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter

cambridge-wtwt/acl2020-wtwt-tweets ACL 2020

We present a new challenging stance detection dataset, called Will-They-Won't-They (WT-WT), which contains 51, 284 tweets in English, making it by far the largest available dataset of the type.

tWT--WT: A Dataset to Assert the Role of Target Entities for Detecting Stance of Tweets

Ayushk4/bias-stance NAACL 2021

The stance detection task aims at detecting the stance of a tweet or a text for a target.

Semi-supervised Stance Detection of Tweets Via Distant Network Supervision

lcs2-iiitd/sands 3 Jan 2022

Detecting and labeling stance in social media text is strongly motivated by hate speech detection, poll prediction, engagement forecasting, and concerted propaganda detection.

MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection

DamiFur/Twitter-semeval2016 SEMEVAL 2016

We describe MITRE's submission to the SemEval-2016 Task 6, Detecting Stance in Tweets.