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Fake News Detection

22 papers with code ยท Natural Language Processing

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HGAT: Hierarchical Graph Attention Network for Fake News Detection

5 Feb 2020

News articles along with other related components like news creators and news subjects can be modeled as a heterogeneous information network (HIN for short).

FAKE NEWS DETECTION REPRESENTATION LEARNING

Fake News Detection by means of Uncertainty Weighted Causal Graphs

4 Feb 2020

In this work, we propose a mechanism to detect fake news through a classifier based on weighted causal graphs.

FAKE NEWS DETECTION

Detecting Fake News with Capsule Neural Networks

3 Feb 2020

This paper aims to use capsule neural networks in the fake news detection task.

FAKE NEWS DETECTION WORD EMBEDDINGS

Two-path Deep Semi-supervised Learning for Timely Fake News Detection

31 Jan 2020

In addition, we build a shared CNN to extract the low level features on both labeled data and unlabeled data to feed them into these two paths.

FAKE NEWS DETECTION

Ginger Cannot Cure Cancer: Battling Fake Health News with a Comprehensive Data Repository

27 Jan 2020

Nowadays, Internet is a primary source of attaining health information.

FAKE NEWS DETECTION

To Transfer or Not to Transfer: Misclassification Attacks Against Transfer Learned Text Classifiers

8 Jan 2020

Thus, our results motivate the need for designing training techniques that are robust to unintended feature learning, specifically for transfer learned models.

FAKE NEWS DETECTION TRANSFER LEARNING

Credibility-based Fake News Detection

2 Nov 2019

By analyzing public fake news data, we show that information on news sources (and authors) can be a strong indicator of credibility.

FAKE NEWS DETECTION

Veritas Annotator: Discovering the Origin of a Rumour

WS 2019

Defined as the intentional or unintentionalspread of false information (K et al., 2019)through context and/or content manipulation, fake news has become one of the most seriousproblems associated with online information(Waldrop, 2017).

FAKE NEWS DETECTION RUMOUR DETECTION

Different Absorption from the Same Sharing: Sifted Multi-task Learning for Fake News Detection

IJCNLP 2019

Recently, neural networks based on multi-task learning have achieved promising performance on fake news detection, which focuses on learning shared features among tasks as complementarity features to serve different tasks.

FAKE NEWS DETECTION MULTI-TASK LEARNING