Social Media Manipulation Awareness through Deep Learning based Disinformation Generation

Abstract

As a digital environment introduced for establishing and enhancing human communication through different social networks and channels, social media continued to develop and spread at an incredible rate making it difficult to find or imagine a concept, technology, or business that does not have or plan to have its social media representation and space. Concurrently, social media became a playground and even a battlefield where different ideas carrying out diverse validity degrees are spread for reaching their target audiences generated by clear and trustable well-known, uncertain, or even evil aimed entities. In the stride carried out for preventing, containing, and limiting the effects of social manipulation of the last two types of entities, proper/effective security awareness is critical and mandatory in the first place. On this behalf, several strategies, policies, methods, and technologies were proposed by research and practitioner communities, but such initiatives take mostly a defender perspective, and this is not enough in cyberspace where the offender is in advantage in attack. Therefore, this research aims to produce social media manipulation security awareness taking the offender stance by generating and analysing disinformation tweets using deep learning. To reach this goal, a Design Science Research methodology is followed in a Data Science approach, and the results obtained are analysed and positioned in the ongoing discourses showing the effectiveness of such approach and its role in building future social media manipulation detection solutions. This research also intends to contribute to the design of further transparent and responsible modelling and gaming solutions for building/enhancing social manipulation awareness and the definition of realistic cyber/information operations scenarios dedicated/engaging large multi-domain (non)expert audiences.