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
AraCOVID19-MFH: Arabic COVID-19 Multi-label Fake News and Hate Speech Detection Dataset
This paper releases "AraCOVID19-MFH" a manually annotated multi-label Arabic COVID-19 fake news and hate speech detection dataset.
Knowledge Graph informed Fake News Classification via Heterogeneous Representation Ensembles
bkolosk1/kbnr
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Increasing amounts of freely available data both in textual and relational form offers exploration of richer document representations, potentially improving the model performance and robustness.
A Stylometric Inquiry into Hyperpartisan and Fake News
The articles originated from 9 well-known political publishers, 3 each from the mainstream, the hyperpartisan left-wing, and the hyperpartisan right-wing.
Some Like it Hoax: Automated Fake News Detection in Social Networks
As a contribution towards this objective, we show that Facebook posts can be classified with high accuracy as hoaxes or non-hoaxes on the basis of the users who "liked" them.
A Two-Level Classification Approach for Detecting Clickbait Posts using Text-Based Features
The detector is based almost exclusively on text-based features taken from previous work on clickbait detection, our own work on fake post detection, and features we designed specifically for the challenge.
On the Benefit of Combining Neural, Statistical and External Features for Fake News Identification
We present a novel idea that combines the neural, statistical and external features to provide an efficient solution to this problem.
The Rise of Guardians: Fact-checking URL Recommendation to Combat Fake News
nguyenvo09/CombatingFakeNews
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The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR 2018, Ann Arbor, MI, USA, July 08-12, 2018 2018
To fill this gap, in this paper, we (i) collect and analyze online users called guardians, who correct misinformation and fake news in online discussions by referring fact-checking URLs; and (ii) propose a novel fact-checking URL recommendation model to encourage the guardians to engage more in fact-checking activities.
Debunking Fake News One Feature at a Time
Identifying the stance of a news article body with respect to a certain headline is the first step to automated fake news detection.
EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection
yaqingwang/EANN-KDD18
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Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2018
One of the unique challenges for fake news detection on social media is how to identify fake news on newly emerged events.
Belittling the Source: Trustworthiness Indicators to Obfuscate Fake News on the Web
To this aim, an important step to detect fake-news is to have access to a credibility score for a given information source.
