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.


Use these libraries to find Fake News Detection models and implementations

Most implemented papers

Fake News Detection on Social Media: A Data Mining Perspective

KaiDMML/FakeNewsNet 7 Aug 2017

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

cair/TsetlinMachine LREC 2022

The proliferation of fake news, i. e., news intentionally spread for misinformation, poses a threat to individuals and society.

Defending Against Neural Fake News

rowanz/grover NeurIPS 2019

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

gordicaleksa/pytorch-GAT 10 Feb 2019

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

entitize/fakeddit 10 Nov 2019

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

sungyongs/CSI-Code 20 Mar 2017

Specifically, we incorporate the behavior of both parties, users and articles, and the group behavior of users who propagate fake news.

FAKEDETECTOR: Effective Fake News Detection with Deep Diffusive Neural Network

jwzhanggy/DifNet 22 May 2018

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

AIRLegend/fakenews 3 Jun 2018

By projecting the explicit and latent features into a unified feature space, TI-CNN is trained with both the text and image information simultaneously.