TrendsBot consists of a virtual assistant (bot) that helps users to identify news of doubtful and / or false origin in groups of the Telegram.
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Updated
Mar 25, 2019 - Python
TrendsBot consists of a virtual assistant (bot) that helps users to identify news of doubtful and / or false origin in groups of the Telegram.
Applying BERT, PhoBERT, RoBERT models to natural language processing to detect official and unofficial news.
The aim of this project is to generate fake news in the Azerbaijani language using LSTM Recurrent Neural Networks. LSTM Recurrent Neural Networks are powerful Deep Learning models which are used for learning sequenced data. Here a LSTM model was trained on 65 thousand samples, and it should be able to generate text.
A news application to check the reliability of the news
API for the browser extension. The API computes scores and stores result in a DB
A python library for fact checking of Fake News with the help of the semantic web
This repo contains predictive models which is ready for deployment.
MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models 20th IEEE International Conference on Data Mining (ICDM)
AntiFa (Anti FakeNews) é um projeto dedicado a analisar e quantificar a porcentagem de notícias falsas (fake news) disseminadas por políticos e personalidades brasileiras em suas redes sociais.
This repository is a research prototype for early detecting fake news shared in social media when evidence or metadata is not present.
News labeling as early as possible: real or fake? Proceedings of the 2019 IEEE/ACM on Advances in Social Networks Analysis and Mining
Full stack Fake News Detection using machine learning code and documents
Rnn (vanial, GRU and LSTM) from scratch
A website to help provide a context for determining the bias of any given news article
This is the repository of Pytorch code and dataset for paper "Learning from Fact-checkers: Analysis and Generation of Fact-checking Language", SIGIR 2019
This is the repository of code and dataset for paper "The Rise of Guardians: Fact-checking URL Recommendation to Combat Fake News", SIGIR 2018
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