ARCA è l'archivio istituzionale ad accesso aperto della ricerca dell'Università Ca' Foscari Venezia e nasce nel 2014 con lo scopo di raccogliere, diffondere e conservare la produzione scientifica dell'Università. Costituisce di fatto il catalogo della ricerca di Ateneo.
ARCA è un sistema Open Access: è non solo consultabile da tutti e visibile nei motori di ricerca, ma anche interoperabile con il portale cerCa. In questo modo vengono valorizzate la produzione scientifica dell'Ateneo e la visibilità dei ricercatori.

Digital traces of conversations in micro-blogging platforms and OSNs provide information about user opinion with a high degree of resolution. These information sources can be exploited to understand and monitor collective behaviors. In this work, we focus on polarization classes, i.e., those topics that require the user to side exclusively with one position. The proposed method provides an iterative classification of users and keywords: first, polarized users are identified, then polarized keywords are discovered by monitoring the activities of previously classified users. This method thus allows tracking users and topics over time. We report several experiments conducted on two Twitter datasets during political election time-frames. We measure the user classification accuracy on a golden set of users, and analyze the relevance of the extracted keywords for the ongoing political discussion.

Digital traces of conversations in micro-blogging platforms and OSNs provide information about user opinion with a high degree of resolution. These information sources can be exploited to understand and monitor collective behaviours. In this work, we focus on polarisation classes, i.e., those topics that require the user to side exclusively with one position. The proposed method provides an iterative classification of users and keywords: first, polarised users are identified, then polarised keywords are discovered by monitoring the activities of previously classified users. This method thus allows tracking users and topics over time. We report several experiments conducted on two Twitter datasets during political election time-frames. We measure the user classification accuracy on a golden set of users, and analyse the relevance of the extracted keywords for the ongoing political discussion.

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