Conspiracy or Not? A Deep Learning Approach to Spot It on Twitter

Abstract

Sentiment analysis is an active topic in Natural Language Processing (NLP). It has attracted a significant interest of research community due to the wide range of applications, including social-media, fake news spotting and interactive applications. In this paper, we present a novel approach for semi-automatic background creation and conspiracy classification. For this purpose, a complete framework including novel recurrent models is proposed. The BORJIS: Best algorithm foR Joint conspiracy and sarcasm detection has been tested on twitter-crawled data and It is composed by: $(a)$ the crawler and labelling module, $(b)$ the features vector extraction and $(c)$ the conspiracy classifier. BORJIS was compared with up-to-date techniques and it showed a significant improvement (≥ 10% accuracy) when applied to diverse datasets.

Keywords

• Fake news detection
• natural language processing
• sentiment analysis
• deep learning