On the Relationship Between Relevance and Conflict in Online Social Link Recommendations
Yanbang Wang, Jon Kleinberg

TL;DR
This paper investigates how social link recommendations influence both relevance and conflict in online networks, revealing that some relevant links can reduce conflict, challenging assumptions about their effects on polarization.
Contribution
It provides the first analysis of the relationship between relevance and conflict in social link recommendations using the Friedkin-Johnsen model.
Findings
Some accurate recommendation algorithms reduce conflict.
Link additions can shift opinion conflict levels.
Not all relevant links increase conflict.
Abstract
In an online social network, link recommendations are a way for users to discover relevant links to people they may know, thereby potentially increasing their engagement on the platform. However, the addition of links to a social network can also have an effect on the level of conflict in the network -- expressed in terms of polarization and disagreement. To this date, however, we have very little understanding of how these two implications of link formation relate to each other: are the goals of high relevance and conflict reduction aligned, or are the links that users are most likely to accept fundamentally different from the ones with the greatest potential for reducing conflict? Here we provide the first analysis of this question, using the recently popular Friedkin-Johnsen model of opinion dynamics. We first present a surprising result on how link additions shift the level of…
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Taxonomy
TopicsOpinion Dynamics and Social Influence · Complex Network Analysis Techniques · Social Media and Politics
