社交网络和大众媒体新媒体外文文献翻译2013年译文3000多字

文献出处：DAVID A. SIEGEL.Social Networks and the Mass Media[J] American Political Science Review.2013,107,(04) pp 786-805

原文

Social Networks and the Mass Media

SIEGEL

Abstract:

How do global sources of information such as mass media outlets, state propaganda, NGOs, and national party leadership affect aggregate behavior? Prior work on this question has insufficiently considered the complex interaction between social network and mass media influences on individual behavior. By explicitly modeling this interaction, I show that social network structure conditions media's impact. Empirical studies of media effects that fail to consider this risk bias. Further, social network interactions can amplify media bias, leading to large swings in aggregate behavior made more severe when individuals can select into media matching their preferences. Countervailing media outlets and social elites with unified preferences can mitigate the effect of bias; however, media outlets promulgating antistatic quo bias have an advantage. Theoretical results such as these generate numerous testable hypotheses; I provide guidelines for deriving and testing hypotheses from the model and discuss several such hypotheses.

Keywords: Business Models, Cloud Computing, Electronic Markets, Resource Sharing, Social network

EMERGENCE OF SOCIAL NETWORKS AND CLOUD COMPUTING

Social networking has become an every day part of many peoples’ lives as evidenced by the huge user communities that are part of such networks. Facebook, for instance, was launched in February 2004 by Harvard under graduate students as an alternative to the traditional student directory. In tended to cover interaction between students at Universities–Facebook enables individuals to encourage others to joint he network through personalized invitations, friend suggestions and creation of specialist groups. Today Facebook has a much wider take up than just students at Universities. Facebook now facilitates interaction between people by enabling sharing of common interests, videos, photos, etc.

Some social network populations exceed that of large countries, for example Facebook has over 350 million active users. Social networks provide a platform to facilitate communication and sharing between users, in an attempt to model real world relationships. Social networking has now also extended beyond communication between friends; for instance, there are a multitude of integrated applications that are now made available by companies, and some organizations use such applications, such as Facebook Connect to authenticate users, i.e. they utilize a user’s Facebook credentials rather than requiring their own credentials(for example the Calgary Airport authority in Canada uses Facebook Connect to grant access to their WiFi network). This ability to combine a third party application (including its local data) to authenticate users demonstrates the service-oriented approach to application development. By tapping into an already established community around a particular social networking platform, it becomes unnecessary to require users to register with another system.

The structure of a Social Network is essentially the formation of a dynamic virtual community with inherent trust relationships between friends. (Szmigin et al., 2006) identify how “relationship marketing” (identified as referring to all marketing activities directed towards establishing, developing and maintaining successful relational exchanges) can be facilitated through the creation of on-line communities. They discuss how on-line communities can be used to facilitate interaction and bonding between consumer and suppliers, intermediate parties and specific brands. Similarly, (Shang et al., 2006) discuss how brand loyalty can be achieved through various types of participation within an on-line community (focusing specifically on the https://www.360docs.net/doc/396077566.html, –a virtual community of Apple users in Taiwan). They discuss the motivation for individuals to promote certain products during on-line discussions (active participants) and for others to remain as lurkers (passive participants). The study particularly focuses on the incentives for participants to contribute to an on-line community, based on the perception of a user about the degree of relevance towards an object that is being discussed –focusing on both cognitive (based on utilitarian motive –concerning an individual’s concern with the cost and benefit of the product or service) and affective (a value-expressive motive, referring to an indiv idual’s interest in enhancing self-esteem or self-conception, and in projecting his/her desired self-image to the outside world through the product or service).

It is also useful to understand, for instance, how such trust relationships could be used as a foundation for resource (information, hardware, services) sharing. Cloud environments are

typically focused on providing low level abstractions of computation or storage. Using this approach, a user is able to access (on a short term/rental basis) capacity that is owned by another person or business (generally over a computer network). In this way, a user is able to outsource their computing requirements to an external provider – limiting their exposure to cost associated with systems management and energy use. Computation and Storage Clouds are complementary and act as building blocks from which applications can be constructed –using a technique referred to as “mash-ups”. Storage Clouds are gaining popularity as a way to extend the capabilities of storage-limited devices such as phones and other mobile devices. There are also a multitude of commercial Cloud providers such as Amazon EC2/S3, Google App Engine, Microsoft Azure and also many smaller scale open clouds like Nimbus (Keahey et al., 2005) and Eucalyptus (Nurmi et al., 2009). A Social Cloud (Chard et al., 2010), on the other hand, is a scalable computing model in which virtualized resources contributed by users are dynamically provisioned amongst a group of friends. Compensation for use is optional as users may wish to share resources without payment, and rather utilize a reciprocal credit (or barter) based model (Andrade et al., 2010). In both cases guarantees are offered through customized Service Level Agreements (SLAs). In a sense, this model is similar to a Volunteer computing approach, in that friends share resources amongst each other for little to no gain. However, unlike Volunteer models there is inherent accountability through existing friend relationships. There are a number of advantages gained by leveraging social networking platforms, in particular one can gain access to huge user communities, can exploit existing user management functionality, and rely on pre-established trust formed through existing user relationships.

The author thanks Jason Barabas, Jon Bendor, Ted Carmines, Jamie Druckman, John Freeman, Matt Golder, Sona Golder, Bob Jackson, Jenn Jerit, Kris Kanthak, ?zge Kemahlioglu, Charlotte Lee, Valerie Martinez-Ebers, Adam Meirowitz, Scott McClurg, Will Moore, Chris Reenock, John Ryan, John Scholz, Jake Shapiro, Anand Sokhey, Jeff Staton, Jim Stimson, Craig Volden, Jon Woon, four very helpful anonymous reviewers, and audiences in the Political Economics group at the Stanford GSB, Political Science departments at FSU, GWU, Minnesota, Pittsburgh, and Stony Brook, and the Frank Batten School of Leadership and Public Policy at UVa. Any errors are my own.

To begin to answer this question, I develop a novel theory of aggregate opinion and

behavior. The theory considers a heterogeneous population of individuals who must choose between dichotomous options. It incorporates the interaction of social network and mass media influences at the individual level; its key assumption is that the more others choose an option, the more one is apt to do so as well. In the theory, social networks provide information about the choices of those to whom one is directly connected, while the mass media provide (potentially biased) information about aggregate choice. The theory thus applies to, for example, voter turnout and political participation (e.g., Gerber, Green, and Larimer 2008; Lake and Huckfeldt 1998; Leighley 1990; McClurg 2003; Rolfe 2012), opinion formation (e.g., Beck et al. 2002; Druckman and Nelson 2003; Huckfeldt and Sprague 1995), protests and social movements (e.g., Kuran 1991; McAdam 1986), and vote choice (e.g., Beck 2002; Huckfeldt and Sprague 1995; Ryan 2011; Sinclair 2012; Sokhey and McClurg 2012).

Three major results follow from this theory. All hold both when individuals treat media identically and when they select into media in line with their preferences. First, understanding the aggregate effect of the media generally requires considering social networks, because social network structure conditions media's impact. For example, additional weak ties between disparate social groups can reduce the media's impact, and the presence of unified social elites can eliminate the media's impact entirely in the aggregate. Empirical studies of media impact that fail to consider media's interaction with social networks risk bias.

Second, social networks can amplify the effect of media bias. A biased media outlet that systematically under- or over-reports a poll of the population by a only a few percentage points can in some cases swing aggregate behavior (e.g., turnout or vote share) by over 20% in either direction due to positive feedback within the network. Open advocates in the media can have a yet larger impact even when not comparatively influential. Unified social elites limit the effect of media bias, but cannot fully counter an advocate; selection into media, made ever easier with technological improvements, tends to enhance the effect of bias. We should therefore expect media bias to become increasingly important to aggregate behavior.

AN INDIVIDUAL-LEVEL THEORY OF AGGREGATE BEHAVIOR

Though I present a theory of aggregate behavior, it is based on individual-level assumptions informed by what we know about the way personal characteristics, social networks, and mass media outlets affect individual behavior. Due to this, the theory can explore the effect that interactions between these three factors have on aggregate behavior. As importantly, the theory incorporates empirically realistic heterogeneity across people in all

three factors.

Additionally, people are exposed to individuals, groups, and organizations external to one's network, such as mass media outlets, state propaganda, national party leaders, NGOs, and Internet personalities. These outlets can provide information, increasing political knowledge.

As this small sampling of large literatures indicates, individuals' decisions are influenced by the information they obtain via both local social networks and global media outlets. However, comparatively little scholarship has explored the three-way interaction of personal characteristics, social networks, and media

In the second type of bias, which I call advocacy, the media outlet simply states a preference for one of the options, providing no information about aggregate support. The goal in advocacy is to sway the population toward one or the other option. As before, many goals could underlie advocacy beyond just the support of a biased media outlet's preferences. Advocacy represents the editorial power of the media or the influence of an external actor; it is a "one-message" model (Zaller 1992).

I focus my analysis in all three sections on the case in which one of the two options is the status quo, and all individuals begin supporting it. For political participation and social movements, the status quo is not participating. For opinion formation and vote choice, the status quo is an existing option such as a policy in place or an incumbent politician, as contrasted with an alternative such as a newly proposed policy or a challenging politician. For simplicity I subsequently call participation the option that is not the status quo; this should be read as "participation in support of" the option that is not the status quo in contexts other than political participation or social movements.

In my analysis I simultaneously vary media strength, network properties, media bias, and, for two outlets, the strength of the L outlet. Though I keep my analysis to two biased outlets, it can easily be extended to multiple biased outlets with the addition of parameters dictating their relative strengths.

译文

社交网络和大众媒体

西格尔

摘要：大众媒体，有很多种类，比如有国家宣传、非政府组织和国家党领导等，诸如此类的环球资源等信息是如何影响聚合行为的呢?之前在这个问题上的一些探索研究，特别是关于社交网络之间的复杂的相互作用和大众媒体对个人行为的影响研究显得不够深入。通过这种显式地交互建模,我展示了社会网络结构信息媒体的影响。之前对媒体影响的实证研究，没有考虑到这种偏见带来的风险，此外，社交网络交互可以放大这种媒体偏见，当个人可以选择进入哪一个媒体来匹配他们自己的偏好时，这将导致大幅波动的聚合行为变得更严重。反补贴媒体和社会精英的统一偏好可以减少这种偏见的影响，然而，媒体颁布这种反补贴对消除这种偏见是有着一定优势的。这些理论研究结果都相应地提出了许多可供测试的假设；我为讨论这些驱动和测试假设的模型提供了一些指导。

关键词:商业模式，云计算，电子市场，资源共享，社交网络

社交网络和云计算的出现

社交网络已经成为许多人每天生活的一部分，即证明了这种网络庞大的用户群体。例如,由哈佛大学毕业生于2004年2月创作的Facebook是作为替代传统学生名录的方式存在的。目的在于覆盖大学-Facebook，使学生个体之间形成互动,鼓励他人通过个性化的邀请、朋友建议和成立专业小组加入网络社交中。今天的Facebook已变得更加广泛而更不仅仅是在大学生中。现在，Facebook可以通过共同兴趣,视频,照片的分享来促进人们之间的互动,一些社会网络人数超过一个大国的人数,例如，Facebook有超过3.5亿的活跃用户。社交网络提供的这个平台能促进用户之间的交流和共享,并试图塑造一个现实世界关系。当前的社交网络也已不再只是朋友与朋友之间的交流沟通;例如,由公司提供的大量的集成应用程序，现在一些组织正使用这些应用程序,比如Facebook对用户进行身份验证,即他们利用用户的Facebook验证身份,而不需要自己的认证信息(例如加拿