Fake News Detection
151 papers with code • 9 benchmarks • 25 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.
Benchmarks
These leaderboards are used to track progress in Fake News Detection
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Libraries
Use these libraries to find Fake News Detection models and implementationsDatasets
LIAR
RealNews
Weibo NER
FakeNewsNet
Snopes
FNC-1
Most implemented papers
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.
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.
Learning Hierarchical Discourse-level Structure for Fake News Detection
hamidkarimi/HDSF
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NAACL 2019
Incorporating hierarchical discourse-level structure of fake and real news articles is one crucial step toward a better understanding of how these articles are structured.
Fake news detection using Deep Learning
The evolution of the information and communication technologies has dramatically increased the number of people with access to the Internet, which has changed the way the information is consumed.
Graph Neural Networks with Continual Learning for Fake News Detection from Social Media
(2) GNNs trained on a given dataset may perform poorly on new, unseen data, and direct incremental training cannot solve the problem---this issue has not been addressed in the previous work that applies GNNs for fake news detection.
Sentimental LIAR: Extended Corpus and Deep Learning Models for Fake Claim Classification
The rampant integration of social media in our every day lives and culture has given rise to fast and easier access to the flow of information than ever in human history.
Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News
nguyenvo09/EMNLP2020
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EMNLP 2020
The search can directly warn fake news posters and online users (e. g. the posters' followers) about misinformation, discourage them from spreading fake news, and scale up verified content on social media.
Transformer based Automatic COVID-19 Fake News Detection System
For our analysis in this paper, we report a methodology to analyze the reliability of information shared on social media pertaining to the COVID-19 pandemic.
The Surprising Performance of Simple Baselines for Misinformation Detection
ComplexData-MILA/misinfo-baselines
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As social media becomes increasingly prominent in our day to day lives, it is increasingly important to detect informative content and prevent the spread of disinformation and unverified rumours.
User Preference-aware Fake News Detection
The majority of existing fake news detection algorithms focus on mining news content and/or the surrounding exogenous context for discovering deceptive signals; while the endogenous preference of a user when he/she decides to spread a piece of fake news or not is ignored.
