Communication across Boundaries: Work, Structure, and Use of Communication Technologies in a Large Organization

Pamela Hinds, Sara Kiesler
1995 Organization science (Providence, R.I.)  
Exponential growth of public information present on the Web in a set of interlinked heterogeneous sources is a consequence of widespread internet expansion and usage [1] . Search engines are the most widely used tools for searching information from the Web, but the general approaches to analyze the information so extracted cannot integrate different sources. The advent of Web 2.0 has added another dimension to the way the data and information is being populated and shared. E-mail had been a
more » ... r communication platform since long but Web 2.0 has provided other platforms like instant messaging and social networking websites (such as Blogs, wikis, web albums), etc. to cater to instant communication needs. These interactions generate a lot of data about personal communications which when combined with search engine results can be used for extraction of social networks in more efficient ways. Understanding and modeling network structures has been a focus of attention in a number of diverse fields, including physics, life sciences, computer science, statistics, and social sciences. Applications of network analysis include friendship and social networks, marketing and recommender systems, the World Wide Web, and disease models, among others. A social network is a structured representation of the social actors (nodes) and their interconnections (ties) [2]. Social network is an aggregation of social groups (communities) that share common interests and therefore include different relationships such as positions, betweenness and closeness among individuals or groups [3]. These communities on the web are steadily emerging and the demand for forming an on demand social network is immense. They help people to find other people sharing the same interest and are available for discussion and collaborations. For example, if a person is searching for specific information, he can look at the interests of people in his social network and get quite relevant references. Social networking services (SNSs) like Facebook, Orkut, LinkedIn and others have become very popular on the Web [4]. An SNS that manages and stores social networks can become a base of information infrastructure in the future [5]. The potential of Social Networking has been utilized to a good extent in the area of personal communication, evident from the growing popularity of these SNSs, but in the area of professional and academic interaction their potential has not been exploited as much. Social networking will 3 play an important role in future personal, organizational and commercial online interaction as well as location and organization of information and knowledge [4]. Social Networks Analysis (SNA) is an interesting research direction to analyze the structures and relationships of social networks, such as analyses of density, centrality and cliques in social network structures and has been an area of focus for the researchers for quite some time [6]. SNA is an essential and important technique to understand the social structures, social relationships and social behaviors. In SNA the main task is usually about how to extract social network from different communication resources (e.g. web, e-mail communication, Internet relay chats, organizational events, conferences, web usage logs, event logs, instant messenger logs, etc.). Construction of the researcher network, for example can benefit many Web mining and social network applications [7]. For example, in this case, if all the profiles of researchers are correctly extracted, we will have a large collection of well-structured data about realworld researchers. The profiles so extracted can help in expert finding for research guidance for new scholars, potential speakers and contributors for conferences, journals, workshops etc. The academic network so extracted can be used in many services, such as finding an appropriate person to introduce or negotiate someone, who one should talk in order to expand his/her network efficiently [8]. The information so extracted can be used for various competitive intelligence tasks like searching for experts, gathering of information, organizations collaborations analysis, countries collaborations analysis or products impacts analysis and determining topics of interest in an academic community [1]. The extracted academic network can also be used for research trend detection/prediction. Trend detection can help a researcher to analyze the thrust area of research in a particular field, what other researchers are doing in that or related field. Trend prediction can help research community to have an idea of the potential research topics/areas in a particular field. Related Work The dramatic increase of popularity of social networks has attracted a lot of research. Factors like social interaction, knowledge exchange, knowledge discovery, ability to capture data about various types of social interactions at a very fine granularity with practically no reporting bias, and availability of data mining techniques for building descriptive and predictive models of social interactions have become key drivers for computer science research in SNA.
doi:10.1287/orsc.6.4.373 fatcat:rvavwonmkve7bazr344thg6bcu