Information patterns and semantic analysis in social networks

Research activity in the following field: the goal of this study is to apply complex network theory and machine learning filtering to social media streams in order to extract relevant information and knowledge from ongoing phenomena. The study will benefit from the exploration of  the network of users  producing the stream of information, and it will add insights on semantic concepts with an emphasis on completeness and semantic filtering of the information stream. The application of machine learning techniques will add an important contribution in the phase of classification and clustering of the discovered topics. Moreover a methodology to discover false online profiles and fake posts will be evaluated as well. Finally the search of fake accounts and false news is related to cybersecurity issues of account management and information authenticity; an overview of social hacking techniques will be applied in the filtering phase. Supervising research activity of PhDs and conducting research seminars.

Proven experience in API Programming and good knowledge of Complex Networks and Big Data Analysis.

6 months