TY  - CONF
T2  - Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval
PB  - ACM
EP  - 948
ID  - eprints3523
N2  - 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.
SN  - 978-1-4503-4069-4
CY  - New York, NY, USA
KW  - algorithm
KW  -  classification
KW  -  controversy
KW  -  hashtags
KW  -  polarization
KW  -  polarized user
KW  -  social networks
KW  -  topic tracking
KW  -  twitter
KW  -  user
Y1  - 2016///
UR  - http://doi.acm.org/10.1145/2911451.2914716
A1  - Coletto, Mauro
A1  - Lucchese, Claudio
A1  - Orlando, Salvatore
A1  - Perego, Raffaele
T3  - SIGIR '16
TI  - Polarized User and Topic Tracking in Twitter
SP  - 945
AV  - none
M2  - New York, USA
ER  -