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
Social media platforms let users share their opinions through textual or multimedia content. In many settings, this becomes a valuable source of knowledge that can be exploited for specific business objectives. Brands and companies often ask to monitor social media as sources for understanding the stance, opinion, and sentiment of their customers, audience and … Continue reading Data Cleaning for Knowledge Extraction and Understanding on Social Media
Our motivation starts from the fact that knowledge in the world continuously evolves, and thus ontologies and knowledge bases are largely incomplete. We explored iterative methods, using the results as new seeds. In this paper we address the following research questions: How does the reconstructed domain knowledge evolve if the candidates of one extraction are recursively used as seeds? How does the reconstructed domain knowledge spread geographically? Can the method be used to inspect the past, present, and future of knowledge? Can the method be used to find emerging knowledge?
FaST – Fashion Sensing Technology - is a project meant to design, experiment with, and implement an ICT tool that could monitor and analyze the activity of Italian emerging Fashion brands on social media.
The knowledge we may try to extract from human-generated content, IoT and Web sources can be dispersed, informal, contradicting, unsubstantiated and ephemeral today, while already tomorrow it may be commonly accepted. The challenge is to capture and create consolidated knowledge that is new, has not been formalized yet in existing knowledge bases, and is buried inside a big, moving target (the live stream of online data). The myth is that existing tools (spanning fields like semantic web, machine learning, statistics, NLP, and so on) suffice to the objective. I explore the problem that one can face along this path.
We organize a crash-course on how the science of urban data can be applied to solve metropolitan issues. The course is a 2 days face-to-face event with teaching sessions, workshops, case study discussions and hands-on activities for non-IT professionals in the field of city management. It is issued in two editions along the year: in … Continue reading Urban Data Science Bootcamp
In our most recent study, we analysed the user behaviour and profile, as well as the textual and visual content posted on social media for art and culture events. The corresponding paper has been presented at CD-MAKE 2017 in Reggio Calabria on August 31st, 2017. Nowadays people share everything on online social networks, from daily … Continue reading Analysis of user behaviour and social media content for art and culture events
Together with the Urbanscope team, we gave a TedX talk on the topics and results of the project here at Politecnico di Milano. The aim of Urbanscope is to systematically produce compelling views on urban systems to foster understanding and decision making.
Knowledge in the world continuously evolves, and ontologies are largely incomplete. We propose a method and a tool for discovering emerging entities by extracting them from social media. Once instrumented by experts through very simple initialization, the method is capable of finding emerging entities; we propose a mixed syntactic + semantic method.
We study spreading of social content in space during live events, measuring the spreading of the event propagation in space. We build didifferent clusters of fashion brands, we characterize several features of propagation in space and we correlate them to the popularity of the brand and temporal propagation.
