OpinionRings : Inferring and visualizing the opinion tendency of socially connected users

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

6 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)11-24
Journal / PublicationDecision Support Systems
Volume75
Early online date23 Apr 2015
Publication statusPublished - Jul 2015

Abstract

Actors (e.g.; people, organizations and nations) of online social networks often express different opinions toward opinion targets (e.g.; products, events and political figures). Extracting and visualizing the distributions of different opinions among actors facilitate policy-makers (e.g.; business managers and government officials) to develop informed decisions promptly. In this paper, by extending the notion of signed networks, we first provide a formal definition of opinion networks which are networks of actors who hold potentially different opinions against specific targets. Another main contribution of our research is the development of a visualization method called OpinionRings to infer and visualize the actual and the potential opinions of different groups of actors. In particular, the proposed OpinionRings method leverages three concentric rings with various colors and widths to highlight different groups of actors and their opinions. One unique feature of the OpinionRings method is that the inclination of an actor, who originally holds a neutral opinion polarity, to adopt a positive or negative opinion polarity can be estimated according to the color of the actor and the distance to other actors with known opinion polarities. A series of objective quantitative experiments and subjective user-based evaluation show that the proposed OpinionRings method significantly outperforms the traditional visualization methods in terms of cohesiveness of displays, informativeness of visualized contents, and inference power of the visualization scheme. The practical implication of our research is that business managers or government officials can apply our proposed computational method to extract and visualize valuable social intelligence from online social networks to facilitate their decision-making processes.

Research Area(s)

  • Opinion analysis, Opinion networks, Opinion prediction, Opinion visualization, OpinionRings