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

TL;DR

This paper introduces a multidimensional analytical framework for studying polarized social media discussions, focusing on sentiment, location, and time, exemplified through Twitter analysis of the refugee crisis in the EU.

Contribution

It presents a novel framework combining sentiment, spatial, and temporal analysis to understand complex social phenomena on social networks, demonstrated through a case study on refugee discussions.

Findings

Differences in sentiment across EU countries, especially the UK.

Sentiment variations correlate with specific events and locations.

Revealed opinion dynamics and prejudices regarding refugees.

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 the 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.