relation: http://eprints.imtlucca.it/3523/
title: Polarized User and Topic Tracking in Twitter
creator: Coletto, Mauro
creator: Lucchese, Claudio
creator: Orlando, Salvatore
creator: Perego, Raffaele
subject: QA75 Electronic computers. Computer science
description: 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.
publisher: ACM
date: 2016
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Coletto, Mauro and Lucchese, Claudio and Orlando, Salvatore and Perego, Raffaele  Polarized User and Topic Tracking in Twitter.  In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2016, New York, USA pp. 945-948.  ISBN 978-1-4503-4069-4.      (2016)  
relation: http://doi.acm.org/10.1145/2911451.2914716
relation: 10.1145/2911451.2914716