Fake News Detection
125 papers with code • 9 benchmarks • 23 datasets
Fake News Detection is a natural language processing task that involves identifying and classifying news articles or other types of text as real or fake. The goal of fake news detection is to develop algorithms that can automatically identify and flag fake news articles, which can be used to combat misinformation and promote the dissemination of accurate information.
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Most implemented papers
"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection
In this paper, we present liar: a new, publicly available dataset for fake news detection.
Fake News Detection on Social Media: A Data Mining Perspective
First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination.
Explainable Tsetlin Machine framework for fake news detection with credibility score assessment
The proliferation of fake news, i. e., news intentionally spread for misinformation, poses a threat to individuals and society.
Defending Against Neural Fake News
We find that best current discriminators can classify neural fake news from real, human-written, news with 73% accuracy, assuming access to a moderate level of training data.
Fake News Detection on Social Media using Geometric Deep Learning
One of the main reasons is that often the interpretation of the news requires the knowledge of political or social context or 'common sense', which current NLP algorithms are still missing.
r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection
We construct hybrid text+image models and perform extensive experiments for multiple variations of classification, demonstrating the importance of the novel aspect of multimodality and fine-grained classification unique to Fakeddit.
CSI: A Hybrid Deep Model for Fake News Detection
Specifically, we incorporate the behavior of both parties, users and articles, and the group behavior of users who propagate fake news.
``Liar, Liar Pants on Fire'': A New Benchmark Dataset for Fake News Detection
In this paper, we present LIAR: a new, publicly available dataset for fake news detection.
FAKEDETECTOR: Effective Fake News Detection with Deep Diffusive Neural Network
This paper aims at investigating the principles, methodologies and algorithms for detecting fake news articles, creators and subjects from online social networks and evaluating the corresponding performance.
TI-CNN: Convolutional Neural Networks for Fake News Detection
By projecting the explicit and latent features into a unified feature space, TI-CNN is trained with both the text and image information simultaneously.