On the Relationship Between Relevance and Conflict in Online Social Link Recommendations

TL;DR

The paper investigates whether relevance-driven online social link recommendations align with reducing opinion conflict. Using the Friedkin-Johnsen model, it derives a closed-form, non-positive change in conflict when adding links and links this reduction to network connectivity via Cheeger constants and degree terms. It then characterizes conflict-minimizing link features, showing partial alignment with relevance since close yet opinion-dissimilar endpoints reduce conflict, and across real data, some relevant algorithms yield better conflict reduction than others. A practical conflict-awareness metric is proposed to quantify how well a recommender aligns with conflict minimization, and empirical results reveal substantial variation and a paradox where decreasing conflict can increase unhappiness, suggesting a nuanced trade-off for real-world deployment.

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 opinion conflict, followed by explanation work that relates the amount of shift to structural features of the added links. We then characterize the gap in conflict reduction between the set of links achieving the largest reduction and the set of links achieving the highest relevance. The gap is measured on real-world data, based on instantiations of relevance defined by 13 link recommendation algorithms. We find that some, but not all, of the more accurate algorithms actually lead to better reduction of conflict. Our work suggests that social links recommended for increasing user engagement may not be as conflict-provoking as people might have thought.

Figures (7)

• Figure 1: A barbell-like social network with cluster and bridge structures, generated from stochastic block model. Different groups of links have different structural features, producing different expected conflict change when added to network, shown in the right panel. The sample means and their $95\%$-intervals are reported based on repeated simulations. Note that this example gives a special network for illustrative purpose, but our interpretations of Thrm.\ref{['thm:distance1']}, \ref{['thm:distance2']} and Cor.\ref{['crl:distance2']} apply to any networks.
• Figure 2: Measurement of conflict awareness and recall for 13 link recommendation algorithms on samples of Reddit and Twitter social networks. The x-axis is the $\eta=\frac{\#\text{negative links}}{\#\text{positive links}}$ that controls class imbalance in the test set.
• Figure 3: Relationship of the important concepts in the Friedkin-Johnsen opinion model.
• Figure 4: Computational validation for Theorem \ref{['thm:cr']}.
• Figure 5: Computational validation for Theorem \ref{['thm:contract']}.

Theorems & Definitions (21)

• Theorem 1
• Theorem 2
• Theorem 3
• Theorem 4
• Corollary 1
• Example 1
• Definition 1
• Proposition 1
• Theorem 5
• proof