• Other research productsclose
• English close
• Digital Humanities and Cultural Heritage close
• European University of Technology close

The paper presents a novel adaptive parametric documentation, modelling and sharing methodology, which aims to achieve a continuous holistic documentation, data processing and sharing process for cultural heritage community, such as architects, engineers, archaeologists, conservators, programmers, fabricators, contest creators, game developers, scholars and common citizens. Thus, the use of advance parametric and building information modelling software allows the processing and specification of all data by creating the 3D models needed for the multidisciplinary experts. Two Cypriot case studies from the medieval time period have been chosen for the development, and evaluation of our proposed methodology in order to investigate the process of modelling and sharing all the given metadata and 3D data. The first one is the Asinou Church, a UNESCO Heritage stone monument in the Troodos Mountains with a unique interior and the Kolossi Castle, a former Crusader stronghold on the west of the city of Limassol on the island of Cyprus.

The Web and the Online Social Network platforms (OSNs) brought a new era in politics. Introduced by Barack Obama’s election campaign in 2008, we observe a rapidly increasing election campaigns to take place on OSNs. Citizens, Academics, Journalists, Influencers, and many more actors, on the other hand, talk about politics on the online media public sphere. This interaction creates enormous and extremely valuable data to be collected and analyzed. Recent advances in Natural Language Processing, allowed the deep contextual understanding of these data, sheeting light on the question of “what” is discussed on OSNs. However, there is huge gap in the literature, and consequently on the available techniques, on approaches that would allow someone to answer the question of “who”, i.e., different groups of users, is talking on OSNs. The aim of this research is to break new ground in public opinion interpretation using Big Data, by answering the question of “who” is discussing on OSNs and Twitter in particular. This will be achieved by proposing and developing an automated process that classifies Twitter’s users into different social actors. The methodology adopted derives from the fields of Machine Learning and Natural Language processing (for tweets classification) accompanied with political communication theories (for identifying categories of social actors). Through the combination of these disciplines, this study proposes a theoretically sound approach, unlike most existing related work in the literature, where classification categories were defined in a random manner. Moreover, the proposed methodology, in contrast to other approaches in the literature, does not rely on the tweet’s content for the classification process; this can introduce bias in cases where tweets will undergo other types of analyses, e.g., sentiment analysis. Given the above, this work proposes a novel and sound approach for classifying tweets into different social actor classes. Completed