会展经济与管理新媒体营销中英文对照外文翻译文献.docx

1、会展经济与管理新媒体营销中英文对照外文翻译文献中英文对照外文翻译(文档含英文原文和中文翻译)原文:Social Networks and the Mass MediaAdapted from: American Political Science Review,2013,107Social 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 inst

2、ance, 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 UniversitiesFacebook enables individuals to encourage others to joint he network through personalized invitations, friend

3、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. Sharing,Some social network populations exceed that of large co

4、untries, 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, ther

5、e 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 users Facebook credentials rather than requiring their own credentials(for example the Calgary Airp

6、ort 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 commu

7、nity 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) identif

8、y 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 facilitat

9、e 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 a virtual community of Apple

10、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 communi

11、ty, 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 individuals concern with the cost and benefit of the product or service) and affective (a value-expressive motive, refer

12、ring to an individuals 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 (infor

13、mation, 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 ne

14、twork). 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 construc

15、ted 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

16、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 o

17、f 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

18、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 platfo

19、rms, 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

20、 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 reviewe

21、rs, 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 the

22、ory 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

23、 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

24、 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

25、 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 a

26、ggregate effect of the media generally requires considering social networks, because social network structure conditions medias impact. For example, additional weak ties between disparate social groups can reduce the medias impact, and the presence of unified social elites can eliminate the medias i

27、mpact entirely in the aggregate. Empirical studies of media impact that fail to consider medias 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

28、 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

29、 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 AGG

30、REGATE BEHAVIORThough 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 interac

31、tions between these three factors have on aggregate behavior. As importantly, the theory incorporates empirically realistic heterogeneity across people in allthree factors.Additionally, people are exposed to individuals, groups, and organizations external to ones network, such as mass media outlets,

32、 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

33、 and global media outlets. However, comparatively little scholarship has explored the three-way interaction of personal characteristics, social networks, and mediaIn 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 aggre