eprintid: 3523
rev_number: 5
eprint_status: archive
userid: 69
dir: disk0/00/00/35/23
datestamp: 2016-08-31 08:47:26
lastmod: 2016-08-31 08:47:26
status_changed: 2016-08-31 08:47:26
type: conference_item
metadata_visibility: show
creators_name: Coletto, Mauro
creators_name: Lucchese, Claudio
creators_name: Orlando, Salvatore
creators_name: Perego, Raffaele
creators_id: mauro.coletto@imtlucca.it
creators_id: 
creators_id: 
creators_id: 
title: Polarized User and Topic Tracking in Twitter
ispublished: pub
subjects: QA75
divisions: CSA
full_text_status: none
pres_type: paper
keywords: algorithm, classification, controversy, hashtags, polarization, polarized user, social networks, topic tracking, twitter, user
abstract: 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.
date: 2016
date_type: published
series: SIGIR '16
publisher: ACM
place_of_pub: New York, NY, USA
pagerange: 945-948
event_title: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval
event_location: New York, USA
event_dates: 2016
event_type: conference
id_number: 10.1145/2911451.2914716
refereed: TRUE
isbn: 978-1-4503-4069-4
book_title: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval
official_url: http://doi.acm.org/10.1145/2911451.2914716
citation:   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)