社交媒体中关系强度的计算和构建

Eric Gilbert, Karrie Karahalios
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引用次数: 5

摘要

人际关系使社交媒体具有社交性。但是,并非所有的关系都是平等的。我们有些同事与我们联系密切,但并不深入;我们有儿时的朋友,我们认为亲密的,即使我们失去了联系。然而,社交媒体对待每个人都是一样的:有些人要么是完全信任的朋友,要么是完全陌生的人,两者之间几乎没有关系。实际上,人际关系在这一范围内无处不在,社会科学已经研究了几十年的一个主题,即纽带强度(tie strength),指的是两个人之间关系的强度。尽管在这方面的研究中有许多令人信服的发现,但社交媒体并没有纳入关系强度或其教训。大多数关于大规模社会现象的研究也是如此。简单地说,我们不理解网上表达的关系的一个基本属性。这本专著从广泛的角度看待这个问题,将领带强度背后的理论与社交媒体的数据结合起来。我们展示了如何从在线社交媒体的数字痕迹中重建纽带强度,以及如何将其作为设计和分析的工具。具体来说,本文做出了两个核心贡献。首先,它提供了一种丰富、高精度和通用的方法,可以从数字痕迹、近代性等痕迹和信息的情感内容中重建联系强度。例如,该模型可以将用户分为强弱关系,准确率接近89%。我们认为,它也为我们提供了一个重新思考社交媒体许多最基本设计元素的机会。接下来,我们将展示一个如何利用纽带强度来重新设计社交媒体的例子:一个对互联网上任何人开放的Twitter应用程序,它将纽带强度作为其设计的核心。通过这个名为We Meddle的应用,我们展示了纽带强度模型可以推广到一个新的网络社区,它可以通过社交媒体解决现实中人们的实际问题。把这本专著看作是连接线上和线下的纽带也许是公平的;也就是说,它将我们在社交媒体上留下的痕迹与我们对现实生活中人际关系的感受联系起来。我们通过思考设计可能采用的其他方法来结束本文,例如社会计算中的领带强度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computing and Building Around Tie Strength in Social Media
Relationships make social media social. But, not all relationships are created equal. We have colleagues with whom we correspond intensely, but not deeply; we have childhood friends we consider close, even if we fell out of touch. Social media, however, treats everybody the same: someone is either a completely trusted friend or a total stranger, with little or nothing in between. In reality, relationships fall everywhere along this spectrum, a topic social science has investigated for decades under the name tie strength, a term for the strength of a relationship between two people. Despite many compelling findings along this line of research, social media does not incorporate tie strength or its lessons. Neither does most research on large-scale social phenomena.Simply put, we do not understand a basic property of relationships expressed online. This monograph takes a wide view of the problem, merging the theories behind tie strength with the data from social media. We show how to reconstruct tie strength from digital traces in online social media, and how to apply it as a tool in design and analysis. Specifically, this article makes two core contributions. First, it offers a rich, high-accuracy and general way to reconstruct tie strength from digital traces, traces like recency and a message's emotional content. For example, the model can split users into strong and weak ties with nearly 89% accuracy. We argue that it also offers us a chance to rethink many of social media's most fundamental design elements. Next, we showcase an example of how we can redesign social media using tie strength: a Twitter application open to anyone on the internet which puts tie strength at the heart of its design. Through this application, called We Meddle, we show that the tie strength model generalizes to a new online community, and that it can solve real people's practical problems with social media. It may be fair to see this monograph as linking the online to the offline; that is, it connects the traces we leave in social media to how we feel about relationships in real life. We conclude the article by reflecting on other ways design might appropriate ideas like tie strength in social computing.
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