PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Coletto M., Esuli A., Lucchese C., Muntean C. I., Nardini F. M., Perego R., Renso C. Sentiment-enhanced multidimensional analysis of online social networks: Perception of the mediterranean refugees crisis. In: ASONAM 2016 - IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (San Francisco, CA, USA, 18-21 August 2016). Proceedings, pp. 1270 - 1277. IEEE, 2016.
 
 
Abstract
(English)
We propose an analytical framework able to investigate discussions about polarized topics in online social networks from many different angles. The framework supports the analysis of social networks along several dimensions: time, space and sentiment. We show that the proposed analytical framework and the methodology can be used to mine knowledge about the perception of complex social phenomena. We selected the refugee crisis discussions over Twitter as a case study. This difficult and controversial topic is an increasingly important issue for the EU. The raw stream of tweets is enriched with space information (user and mentioned locations), and sentiment (positive vs. negative) w.r.t. refugees. Our study shows differences in positive and negative sentiment in EU countries, in particular in UK, and by matching events, locations and perception, it underlines opinion dynamics and common prejudices regarding the refugees.
URL: http://ieeexplore.ieee.org/document/7752401/
DOI: 10.1109/ASONAM.2016.7752401
Subject Twitter
Data mining
Urban areas
Multidimensional analysis
Refugee crisis
Sentiment analysis
H.3.3 INFORMATION STORAGE AND RETRIEVAL. Information Search and Retrieval
68U35 Information systems


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