PAN 2020: Fake News Spreader Detection Using Neural Tweet Aggregation
Channel: webis
Date: 2020-09-25

Fake News Spreader Detection Using Neural Tweet Aggregation—Notebook for PAN at CLEF 2020

Oleg Bakhteev, Aleksandr Ogaltsov, and Petr Ostroukhov

The paper describes the neural networks-based approach for Profiling Fake News Spreaders on Twitter task at PAN 2020. The problem is reduced to the binary classification with a set of tweets of the user as an object to classify and class labels corresponding to users that are likely to spread fake news and ordinary users. To deal with a set of tweets we employ two neural network architectures: either based on recurrent or convolutional neural networks. We try aggregate the whole information obtained from the tweets to decide whether the user can spread fake news or not. We also present an ensemble of models that consists of a neural network and a classification model that works on each tweet separately.

Paper: https://pan.webis.de/publications.html#bakhteev_2020
Workshop: https://pan.webis.de