The world is facing a new era in which social media communication plays a fundamental role in people's lives. Along with irrefutable benefits, several collateral drawbacks have risen, one being the wide spread of false information with malicious intents, what is now commonly called "Fake News". The fight against this problem is not easy, specially when taking into account the nature of text messages involved on social media platforms (a sea of small messages and myriad users). To address this scenario, this research project aims to solve two important and difficult problems: authorship attribution and phylogeny analysis of small text messages posted on social media platforms, showing how these solutions can help in the identification of false information spread on social networks. Differently from what has been done with longer texts, we will rely upon data-driven approaches, exploiting the recent advances of deep neural networks in the field of pattern recognition. By trying to solve an ever-growing problem of our society, this research will produce outcomes that can be used by many actors, mostly social media platforms, which can directly apply the techniques developed and bring higher reliability and also transparency to the published data. This research can also provide appropriate tools to achieve another level of understanding regarding online posts, taking into consideration their spread and power of influence.
News published in Agência FAPESP Newsletter about the scholarship: