On the Relationship Between Relevance and Conflict in Online Social Link Recommendations
NeurIPS(2023)
摘要
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.
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关键词
online social link
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