Breaking news! We just published our new MOOC "Data Science for Business Innovation" on Coursera! Our course is available for free on Coursera and is jointly offered by Politecnico di Milano and EIT Digital, as a compendium of the must-have expertise in data science for non-technical people, including executives, middle-managers to foster data-driven innovation. The … Continue reading Data Science for Business Innovation. A new MOOC on Coursera
Social networks are huge continuous sources of information that can be used to analyze people's behavior and thoughts. Our goal is to extract such information and predict political inclinations of users. In particular, we investigate the importance of syntactic features of texts written by users when they post on social media. Our hypothesis is that … Continue reading Content-based Classification of Political Inclinations of Twitter Users
Online social media are changing the news industry and revolutionizing the traditional role of journalists and newspapers. In this scenario, investigating the behaviour of users in relationship to news sharing is relevant, as it provides means for understanding the impact of online news, their propagation within social communities, their impact on the formation of opinions, … Continue reading News Sharing Behaviour on Twitter. A Dataset and a Pipeline
We exploit the network that builds around the brands by encoding it into a graph model. We build a social network graph, considering user nodes and friendship relations; then we compare it with a heterogeneous graph model.
A few days ago, politico.eu published a preview of the document that the European Union will issue as guidance for ethical issues related to artificial intelligence and machine learning. The document was written by the High-level Expert Group on Artificial Intelligence, appointed by the European Commission. This advanced version of the document is available online now … Continue reading A sneak peek at the European Union Ethics Guidelines for AI
Topic modeling techniques have been applied in many scenarios in recent years, spanning textual content, as well as many different data sources. The existing researches in this field continuously try to improve the accuracy and coherence of the results. Some recent works propose new methods that capture the semantic relations between words into the topic … Continue reading Improving Topic Modeling with Knowledge Graph Embeddings
Here is a presentation that summarizes some of the relevant topics currently available for theses within the Data Science Lab under my supervision. [slideshare id=133615547&doc=brambilla-datascience-thesis-proposals-feb2019-190227221103] Feel free to get in touch in case you are interested.
We implemented an RNN-based analysis that aim to gauge local support for the two major US political parties in the 68 most competitive House of Representative districts during the 2018 U.S. mid-term elections.
a "nighttime" multidisciplinary interactive course that introduces data science concepts, methods and use cases
Predicting the outcome of elections is a topic that has been extensively studied in political polls, which have generally provided reliable predictions by means of statistical models. In recent years, online social media platforms have become a potential alternative to traditional polls, since they provide large amounts of post and user data, also referring to … Continue reading Understanding Polarized Political Events through Social Media Analysis
