Fake News Detection on Social Media: A Data Mining Perspective

7 Aug 20172 code implementations

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.

FAKE NEWS DETECTION

A simple but tough-to-beat baseline for the Fake News Challenge stance detection task

11 Jul 20176 code implementations

Identifying public misinformation is a complicated and challenging task.

STANCE DETECTION

Fighting Fake News: Image Splice Detection via Learned Self-Consistency

ECCV 2018 1 code implementation

In this paper, we propose a learning algorithm for detecting visual image manipulations that is trained only using a large dataset of real photographs.

A Retrospective Analysis of the Fake News Challenge Stance Detection Task

13 Jun 20187 code implementations

To date, there is no in-depth analysis paper to critically discuss FNC-1's experimental setup, reproduce the results, and draw conclusions for next-generation stance classification methods.

STANCE DETECTION

A Retrospective Analysis of the Fake News Challenge Stance-Detection Task

COLING 2018 1 code implementation

To date, there is no in-depth analysis paper to critically discuss FNC-1{'}s experimental setup, reproduce the results, and draw conclusions for next-generation stance classification methods.

STANCE DETECTION

Learning to Detect Fake Face Images in the Wild

24 Sep 20181 code implementation

Although Generative Adversarial Network (GAN) can be used to generate the realistic image, improper use of these technologies brings hidden concerns.

FACE SWAPPING FAKE IMAGE DETECTION IMAGE GENERATION

In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking

7 Jun 20181 code implementation

The new developments in deep generative networks have significantly improve the quality and efficiency in generating realistically-looking fake face videos.

FACE SWAPPING

Learning Hierarchical Discourse-level Structure for Fake News Detection

NAACL 2019 2 code implementations

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

Combining Similarity Features and Deep Representation Learning for Stance Detection in the Context of Checking Fake News

2 Nov 20181 code implementation

Specifically, we use bi-directional Recurrent Neural Networks, together with max-pooling over the temporal/sequential dimension and neural attention, for representing (i) the headline, (ii) the first two sentences of the news article, and (iii) the entire news article.

DOCUMENT CLASSIFICATION NATURAL LANGUAGE INFERENCE REPRESENTATION LEARNING STANCE DETECTION