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Communications of the ACM

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Algorithm Can Create a Bridge Between Clinton and Trump Supporters


Charting endorsement networks in the U.S. presidential election.

An algorithmic solution could minimize societal polarization by linking people with opposing viewpoints and assessing them on Twitter.

Credit: Kiran Garimella/Aalto University

Scientists at Aalto University in Finland propose an algorithmic solution to minimize societal polarization by linking people with opposing viewpoints and assessing them on Twitter.

Modeling user interactions around a given controversial subject on Twitter on an endorsement graph, with nodes representing Twitter users, tends to yield a strongly biclustered structure.

Aalto professor Aristides Gionis says the algorithm "can be applied on a large scale and is language- and domain-independent. The main algorithm is based on the finding that for a special type of network simulating a polarized network, the best bridges we can add to the network are between the nodes with the highest degrees on either side."

High-degree users are typically well-known and have numerous followers, and the algorithm's application to discussions on U.S. election results suggests "creating a bridge between @hillaryclinton and @breitbartnews would reduce polarization the most," says Aalto's Kiran Garimella.

From Aalto University
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Abstracts Copyright © 2017 Information Inc., Bethesda, Maryland, USA


 

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