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
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.
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.
Automated fact-checking based on machine learning is a promising approach to identify false information distributed on the web.
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.
Detecting and labeling stance in social media text is strongly motivated by hate speech detection, poll prediction, engagement forecasting, and concerted propaganda detection.