Facebook 用户如何在朋友间分配注意力

个人社交网络是社会学研究中的基本对象,研究中的焦点是个人网络的大小(联系人数量)和他们的成分(如亲友和同事)。本文提出了一个个人网络分析的新测度方法:考察个体如何分配其在不同联系人间如何分配注意力。这种研究进路将人们分成两类:将大部分的互动放在很小一部分密友身上的人;将注意力分散到更多联系人身上的人。

本研究所使用的数据是 Facebook 上的用户网络,研究者发现“注意力平衡”(balance of attention)是一个相对稳定的历时性特质,它展现了不同人群和不同互动类型的有趣分野。值得注意的是,基于交流(Communication)的活动比仅仅是基于观察的活动牵涉到更大的注意力集中程度;这两种模式,无语伦在群内还是群际互动中,都呈现出不同的类型特征。最后,研究者对比了个人施予最频繁互动的联系人身上的注意力数量,和这些频繁联系人的变化率,提供了一个测度的方法。

 

原文题目:Center of Attention: How Facebook Users Allocate Attention Across Friends

原文:

Abstract
An individual’s personal network — their set of social contacts—is a basic object of study in sociology. Studies of personal networks have focused on their size (the number of contacts) and their composition (in terms of categories such as kin and co-workers). Here we propose a new measure for the analysis of personal networks, based on the way in which an individual divides his or her attention across contacts. This allows us to contrast people who focus a large fraction of their interactions on a small set of close friends with people who disperse their attention more widely. Using data from Facebook, we find that this balance of attention is a relatively stable property of an individual over time, and that it displays interesting variation across both different groups of people and different modes of interaction. In particular, activities based on communication involve a much higher focus of attention than activities based simply on observation, and these two types of modalities also exhibit different forms of variation in interaction patterns both within and across groups. Finally, we contrast the amount of attention paid by individuals to their most frequent contacts with the rate of change in the identities of these contacts, providing a measure of churn for this set. 

Conclusion
We have provided a way of analyzing individuals’ personal networks in terms of the way they balance their attention across social contacts. This measure exposes properties that are distinct from traditional analyses of personal networks based on size and composition, and it enables a comparison of different interaction modalities and different patterns within and between groups. In addition, the measure has important practical implications: by modeling an individual’s balance of social attention, product designers can properly tailor that individual’s experience to match her preferences for keeping in touch mostly with her top contacts, or with a more diverse set of people.

While our analysis here is based on Facebook data, the framework is very general, and can be applied to any context where detailed interaction data is available, including other social media sites as well as communication modalities such as phone and e-mail. It is an interesting open question to see how the balance of social attention varies across different domains, and in principle these measures can provide a way of categorizing such domains as more focused or more dispersed. It also becomes promising to consider using the balance of attention as a potential feature of individuals in userbased classification and learning tasks, since we have seen that it captures sources of variation among individuals in ways that other measures may miss. Finally, just as measures of network topology can be used to classify different networks into particular archetypes (Newman and Park 2003; Kwak et al. 2010), this measure might prove useful for distinguishing between different types of social environments.  

原文作者:
Lars Backstrom, Facebook lars@fb.com
Eytan Bakshy, University of Michigan, Facebook, ebakshy@fb.com
Jon Kleinberg, Cornell University, kleinber@cs.cornell.edu
Thomas M. Lento, Facebook, tlento@fb.com
Itamar Rosenn, Facebook, itamar@fb.com


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