Sentiment-enhanced Multidimensional Analysis of Online Social Networks:
Perception of the Mediterranean Refugees Crisis

Mauro Coletto, IMT, Lucca, Italy
Andrea Esuli, Nemis Lab., ISTI-CNR, Pisa, Italy
Claudio Lucchese, HPC Lab., ISTI-CNR, Pisa, Italy
Cristina Ioana Muntean,HPC Lab., ISTI-CNR, Pisa, Italy
Franco Maria Nardini, HPC Lab., ISTI-CNR, Pisa, Italy
Raffaele Perego, HPC Lab., ISTI-CNR, Pisa, Italy
Chiara Renso, HPC Lab., ISTI-CNR, Pisa, Italy

July 12 2016

Accepted at SNAST ’16: Workshop on Social Network Analysis Surveillance Technologies in conjunction with ASONAM ’16: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining [1].

Abstract. 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.

References

[1]   Mauro Coletto, Andrea Esuli, Claudio Lucchese, Cristina Ioana Muntean, Franco Maria Nardini, Raffaele Perego, and Chiara Renso. Sentiment-enhanced multidimensional analysis of online social networks: Perception of the mediterranean refugees crisis. SNAST ’16: Workshop on Social Network Analysis Surveillance Technologies, colocated with ASONAM ’16: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2016.

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