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In the past decade, the rapid spread of large volumes of online information among an increasing number of social network users is observed. It is a phenomenon that has often been exploited by malicious users and entities, which forge, distribute, and reproduce fake news and propaganda. In this paper, we present a novel approach to the automatic detection of fake news on Twitter that involves (a) pairwise text input, (b) a novel deep neural network learning architecture that allows for flexible input fusion at various network layers, and (c) various input modes, like word embeddings and both linguistic and network account features. Furthermore, tweets are innovatively separated into news headers and news text, and an extensive experimental setup performs classification tests using both. Our main results show high overall accuracy performance in fake news detection. The proposed deep learning architecture outperforms the state-of-the-art classifiers, while using fewer features and embeddings from the tweet text.

Mining social web text has been at the heart of the Natural Language Processing and Data Mining research community in the last 15 years. Though most of the reported work is on widely spoken languages, such as English, the significance of approaches that deal with less commonly spoken languages, such as Greek, is evident for reasons of preserving and documenting minority languages, cultural and ethnic diversity, and identifying intercultural similarities and differences. The present work aims at identifying, documenting and comparing social text data sets, as well as mining techniques and applications on social web text that target Modern Greek, focusing on the arising challenges and the potential for future research in the specific less widely spoken language.

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