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Modelling majority and expert influences on opinion formation in online social networks
Das R., Kamruzzaman J., Karmakar G. World Wide Web21 (3):663-685,2018.Type:Article
Date Reviewed: May 30 2018

This paper presents an interesting and novel approach to more accurately solving the problem of opinion formation. The authors “introduce the concept of consistency in opinions and associated expertise levels of neighboring agents and embed metrics derived from those to update an agent’s own opinion as well as its expertise level.” In their development, they include three possible groupings of opinion motivators: the majority, experts, and the combined influence of the two. Using statistical methods, they developed expressions to calculate values for their model. To demonstrate the validity of the model, they analyzed opinions on Twitter about whether infant vaccinations cause autism. Using the expressions generated for the validity of their data, they derived the root-mean-square error (RMSE) and directional symmetry (DS).

The authors compare their method’s results with those of two extant methodologies, the biased voter model and the Moussaid model; their model performed better than the other two. They go on to do an analysis of the majority versus expert influences to further demonstrate how much more accurate their model is compared to the Moussaid model. They then analyze the effects of changing the bounding values used in their analysis. Finally, they discuss how their “model captures consensus, polarization, and fragmentation properties of opinion dynamics.”

Other than having a number of grammatical errors, this is a valuable paper of consequence to anyone in the opinion gathering and analyzing field.

Reviewer:  Michael Moorman Review #: CR146051 (1808-0444)
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