Effects of Gatekeeping on the Diffusion of Information

In the main, investigation of the structure of social networks has been in the area of social search (Milgram, 1967; Granovetter, 1973; Killworth & Bernard, 1978; Adamic & Adar, 2005). Schnettler (2009) points out that the diffusion process in social networks is not well understood. Rogers (2003) defines diffusion as the process by which information is spread through a network. From a practical perspective, understanding the diffusion process is useful because it shows how information spreads through a marketplace or social community. This could involve a disease, a new product or personal information. This knowledge allows researchers to strategically place individuals in the network to disseminate, regulate, or prevent the spread of the information. While this process has been theorized (Rogers, 2003), few behavioral studies have examined it. A likely reason for this because the phenomenon is difficult to observe in the field, as knowledge of the boundaries of a social network is necessary and often unknown. Therefore, empirical research is needed in this area. Gatekeeping theory provides a framework from which to study the process of information diffusion. Gatekeeping is the process of information control (Lewin, 1951). Gates are decision or action points and gatekeepers are those individuals responsible for making the information control decisions (Shoemaker & Vos, 2009). Also, forces exist at each gate that influence the decision process (Lewin, 1951). Further, Lewin (1951) argues that any network actor can be a gatekeeper. This suggests that being a gatekeeper is a role one takes on. Shoemaker and Vos (2009) argue that gatekeepers are not equally influential in the process of information control. At the point that information is received, the actor must decide whether or not to pass the information along through the network and at each gate forces exist (Shoemaker & Vos, 2009). Forces can be either positive or negative and their polarity is not constant (Lewin, 1951) and these forces can be in competition with one another (Shoemaker & Vos, 2009). At the point information is received, the network actor must decide whether or not to pass the information along through the network. Two forces that are relevant to the diffusion process are the activity level of the gatekeepers and the network location of the gatekeepers. The landing of the Mars Rover, Curiosity, was followed using Twitter hashtags to observe the effect of gatekeeper activity level and gatekeeper location on information diffusion. The advantage of using Twitter data is that a social network can be visualized using the followed/follower relationships. Diffusion paths can then be visualized atop the social network, which allows one to observe the network influentials and structural holes. Using the Twitter API, tweets were collected from July 1 through September 30. Curiosity hashtags will be mined using a datamining approach. Structural equation modeling will be employed to analyze the data. 2. C. Sean Burns (245) University of Missouri Title Google Scholar and Free or Open Access Scholarly Content: Impact on Academic Libraries Abstract This study examines the bibliographic references that users of a social computing web application collect in order to measure the probability of the use of non-library electronic discovery services and open access content. The purpose is to understand the strategic future of academic libraries.This study examines the bibliographic references that users of a social computing web application collect in order to measure the probability of the use of non-library electronic discovery services and open access content. The purpose is to understand the strategic future of academic libraries. In 2010 Ithaka S+R (Schonfeld & Housewright) published the results of a 2009 survey that asked faculty about their scholarly communication behaviors and attitudes. The research report claims that information seeking "practices have shifted rapidly in recent years, and as a result the academic library is increasingly being disintermediated from the discovery process, risking irrelevance in one of its core functional areas" (p. 2). Specifically, the report suggests researchers in the sciences, social sciences, and the humanities are moving away from the library building, the librarians, and the library's services and towards general purpose search engines and other electronic resources to satisfy their document needs. In turn, these movements supposedly result in a less relevant academic library. For example, although search and discovery through electronic services include those subscribed by the library, the report reveals, at the network level, the heavy use of non-library electronic resource discovery services. For instance, searching with Google and Google Scholar, both counted here as non-library electronic resource discovery services, ranked third place in the discovery process (~70%), behind searching electronic, full text databases (~90%), and following citations (~90%), a process referred to as chaining (Ellis, Cox, & Hall, 1993). The Ithaka report does not identify an additional concern regarding the state of affairs at the network level. Academic library users may not just use alternate services for document discovery but these alternate services may lead to full text electronic documents, such as open access content. That is, we may ask whether open access content, in combination with changing information seeking "practices," makes it possible to bypass the library's collections, in addition to its discovery services, and whether this results in another kind of disintermediation. There are two key questions to ask. The first question has two parts and concerns how much content is freely accessible via scholarly search engines, such as Google Scholar, and how influential is that content (as measured by citation counts). The second question is conditional on the first. If Google Scholar is able to locate many full text scholarly documents, then what does that mean for the strategic future of academic libraries? In order to answer the first question, this dissertation is based on a systematic random sample (n = 999) of bibliographic references, collected by users of a social reference management application, and longitudinal (three-year) bibliometric data of these references, which was collected from Google Scholar. To answer the second question, the bibliometric findings are interpreted within a decision theoretical framework aided by Herbert Simon's (1955) notion of bounded rationality. Specifically, the theory offers an explanation for why users may prefer and choose one discovery service over another given that people operate with "limited knowledge and ability" (p. 114). References Ellis, D. , Cox, D. , & Hall, K. (1993). A comparison of the information seeking patterns of researchers in the physical and social sciences. Journal of Documentation, 49(4), 356-369. Schonfeld, R.C. , & Housewright, R. (2010). Faculty survey 2009: Key strategic insights for libraries, publishers, and societies. Retrieved from Ithaka website: http://www.sr.ithaka.org/research-publications/faculty-survey-2009 Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99-118. 3. Barbara W. Burton (178)