论文标题

友谊悖论和社交网络参与

The Friendship Paradox and Social Network Participation

论文作者

Medhat, Ahmed, Iyer, Shankar

论文摘要

友谊悖论意味着一个人平均而言,朋友的朋友比朋友的朋友少。先前的工作表明,友谊悖论如何导致与与朋友数量相关的行为的看法偏见:例如,人们倾向于认为自己的朋友比他们更社交。在这里,我们研究了在线社交网络中内容创建(“共享”)概念设置中这种社交比较的后果。假设人们将其内容收到的反馈数量与朋友的内容收到的反馈数量进行比较,并假设他们是通过比较而修改其共享行为的。随着时间的推移,这如何影响社交网络上的总体共享?假设最初的共享和反馈率,我们对模型生成的合成网络进行模拟。因此,人们对社会比较的最初对共享行为的最初修改完全由友谊悖论驱动。这些修改会导致共享率的不均匀性,从而进一步改变了感知偏见。如果人们对社会比较的反应是单调的(即,差异越大,共享行为的修改越大),我们的模拟表明,网络中的总体共享逐渐下降。同时,凸反应可以维持或增长整体共享网络。我们完全专注于当前工作中的合成图,尚未将模拟扩展到现实世界网络拓扑。然而,我们确实讨论了实际含义,例如即使在存在不利的社会比较效果的情况下,如何量身定制干预措施以维持长期共享。

The friendship paradox implies that a person will, on average, have fewer friends than their friends do. Prior work has shown how the friendship paradox can lead to perception biases regarding behaviors that correlate with the number of friends: for example, people tend to perceive their friends as being more socially engaged than they are. Here, we investigate the consequences of this type of social comparison in the conceptual setting of content creation ("sharing") in an online social network. Suppose people compare the amount of feedback that their content receives to the amount of feedback that their friends' content receives, and suppose they modify their sharing behavior as a result of that comparison. How does that impact overall sharing on the social network over time? We run simulations over model-generated synthetic networks, assuming initially uniform sharing and feedback rates. Thus, people's initial modifications of their sharing behavior in response to social comparisons are entirely driven by the friendship paradox. These modifications induce inhomogeneities in sharing rates that can further alter perception biases. If people's responses to social comparisons are monotonic (i.e., the larger the disparity, the larger the modification in sharing behavior), our simulations suggest that overall sharing in the network gradually declines. Meanwhile, convex responses can sustain or grow overall sharing in the network. We focus entirely on synthetic graphs in the present work and have not yet extended our simulations to real-world network topologies. Nevertheless, we do discuss practical implications, such as how interventions can be tailored to sustain long-term sharing, even in the presence of adverse social-comparison effects.

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