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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 <italic>BORJIS</italic>: Best algorithm foR Joint conspiracy and sarcasm detection has been tested on twitter-crawled data and It is composed by: <inline-formula> <tex-math notation="LaTeX">$(a)$ </tex-math></inline-formula> the crawler and labelling module, <inline-formula> <tex-math notation="LaTeX">$(b)$ </tex-math></inline-formula> the features vector extraction and <inline-formula> <tex-math notation="LaTeX">$(c)$ </tex-math></inline-formula> the conspiracy classifier. <italic>BORJIS</italic> was compared with up-to-date techniques and it showed a significant improvement (&#x2265; 10&#x0025; accuracy) when applied to diverse datasets.

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