Combating fake news with adversarial domain adaptation and neural models

Abstract

Factually incorrect claims on the web and in social media can cause considerable damage to individuals and societies by misleading them. As we enter an era where it is easier than ever to disseminate "fake news" and other dubious claims, automatic fact checking becomes an essential tool to help people discern fact from fiction. In this thesis, we focus on two main tasks: fact checking which involves classifying an input claim with respect to its veracity, and stance detection which involves determining the perspective of a document with respect to a claim. For the fact checking task, we present Bidirectional Long Short Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN) based models and conduct our experiments on the LIAR dataset [Wang, 2017], a recently released fact checking task. Our model outperforms the state of the art baseline on this dataset. For the stance detection task, we present bag of words (BOW) and CNN based models in hierarchy schemes. These architectures are then supplemented with an adversarial domain adaptation technique, which helps the models overcome dataset size limitations. We test the performance of these models by using the Fake News Challenge (FNC) [Pomerleau and Rao, 2017], the Fact Extraction and VERification (FEVER) [Thorne et al., 2018], and the Stanford Natural Language Inference (SNLI) [Bowman et al., 2015] datasets. Our experiments yielded a model which has state of the art performance on FNC target data by using FEVER source data coupled with adversarial domain adaptation [Xu et al., 2018].