Istituto di Scienza e Tecnologie dell'Informazione     
Coletto M., Lucchese C., Orlando S., Perego R. Polarized user and topic tracking in twitter. In: SIGIR '16 - 39th International ACM SIGIR conference on Research and Development in Information Retrieval (Pisa, Italy, 17-21 July 2016). Proceedings, pp. 945 - 948. ACM, 2016.
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.
URL: http://dl.acm.org/citation.cfm?doid=2911451.2914716
DOI: 10.1145/2911451.2914716
Subject Online Social Networks
Topic Tracking
H.3.3 INFORMATION STORAGE AND RETRIEVAL. Information Search and Retrieval

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