Stance Detection
82 papers with code • 6 benchmarks • 21 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.
Example:
- Source: "Apples are the most delicious fruit in existence"
- Reply: "Obviously not, because that is a reuben from Katz's"
- Stance: deny
Benchmarks
These leaderboards are used to track progress in Stance Detection
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LIAR
x-stance
VAST
COVID-CQ
Most implemented papers
A simple but tough-to-beat baseline for the Fake News Challenge stance detection task
Identifying public misinformation is a complicated and challenging task.
A Retrospective Analysis of the Fake News Challenge Stance Detection Task
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
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
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
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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
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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
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
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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
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Detecting and labeling stance in social media text is strongly motivated by hate speech detection, poll prediction, engagement forecasting, and concerted propaganda detection.
Infusing Knowledge from Wikipedia to Enhance Stance Detection
zihaohe123/wiki-enhanced-stance-detection
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WASSA (ACL) 2022
Stance detection infers a text author's attitude towards a target.
