Document - Sentiment-enhanced multidimensional analysis of online social networks: perception of the mediterranean refugees crisis

2016

Conference article Open Access

Sentiment-enhanced multidimensional analysis of online social networks: perception of the mediterranean refugees crisis

Coletto M., Esuli A., Lucchese C., Muntean C. I., Nardini F. M., Perego R., Renso C.

Computer Science - Social and Information Networks Sentiment analysis Multidimensional analysis FOS: Computer and information sciences Urban areas Refugee crisis Social and Information Networks (cs.SI) Twitter Data mining

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.

Source: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 1270–1277, San Francisco, CA, USA, 18-21 August 2016

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BibTeX entry

@inproceedings{oai:it.cnr:prodotti:366973,
	title = {Sentiment-enhanced multidimensional analysis of online social networks: perception of the mediterranean refugees crisis},
	author = {Coletto M. and Esuli A. and Lucchese C. and Muntean C. I. and Nardini F. M. and Perego R. and Renso C.},
	doi = {10.1109/asonam.2016.7752401 and 10.48550/arxiv.1605.01895},
	booktitle = {IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 1270–1277, San Francisco, CA, USA, 18-21 August 2016},
	year = {2016}
}