Nowadays people share everything on online social networks, from daily life stories to the latest local and global news and events. Many researchers have exploited this as a source for understanding the user behaviour and profile in various settings. In this paper, we address the specific problem of user behavioural profiling in the context of cultural and artistic events. We propose a specific analysis pipeline that aims at examining the profile of online users, based on the textual content they published online. The pipeline covers the following aspects: data extraction and enrichment, topic modeling, user clustering, and prediction of interest. We show our approach at work for the monitoring of participation to a large-scale artistic installation that collected more than 1.5 million visitors in just two weeks (namely The Floating Piers, by Christo and Jeanne-Claude). We report our findings and discuss the pros and cons of the work.

Analysis of online user behaviour for art and culture events

RAHDARI, BEHNAM;ARABGHALIZI, TAHEREH;Brambilla, Marco
2017-01-01

Abstract

Nowadays people share everything on online social networks, from daily life stories to the latest local and global news and events. Many researchers have exploited this as a source for understanding the user behaviour and profile in various settings. In this paper, we address the specific problem of user behavioural profiling in the context of cultural and artistic events. We propose a specific analysis pipeline that aims at examining the profile of online users, based on the textual content they published online. The pipeline covers the following aspects: data extraction and enrichment, topic modeling, user clustering, and prediction of interest. We show our approach at work for the monitoring of participation to a large-scale artistic installation that collected more than 1.5 million visitors in just two weeks (namely The Floating Piers, by Christo and Jeanne-Claude). We report our findings and discuss the pros and cons of the work.
2017
Machine Learning and Knowledge Extraction. CD-MAKE 2017.
9783319668079
Behaviour analysis; Big data; Data mining; Social media; Theoretical Computer Science; Computer Science (all)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1059301
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