The Comparability of Event-Related and General Social Support

Some studies (e.g., Kogovšek & Hlebec, 2008, 2009) have shown that the name generator and the role relation approaches to measuring social networks are to some extent comparable, but less so the name generator and the event-related approaches (Hlebec, Mrzel, & Kogovšek, 2009). In this chapter, the composition of the social support network assessed by both the general social support approach and the event-related approach (support during 15 major life events) is analyzed and compared. In both cases, the role relation approach is used. In addition, in both approaches a more elaborate (16 possible categories ranging from partner, mother, father, friend to no one) and a more simple (6 possible categories ranging from family member, friend, neighbor to no one) response format is applied and compared. The aim of the chapter is to establish, in a controlled quasi-experiment setting, whether the different approaches (i.e. the general social support and the event-related approach) produce similar social networks regardless of the response format (long vs. short).

Mining Organizations’ Networks

This chapter presents six examples of organization-related social network mining: 1) interorganizational and sentiment networks in the Deepwater BP Oil Spill events, 2) intraorganizational interdepartmental networks in the Savannah College of Art and Design (SCAD), 3) who-to-whom email networks across the organizational hierarchy the Ford Motor Company’s automotive engineering innovation: “Sync® w/ MyFord Touch”, 4) networks of selected individuals who left that organization, 5) semantic associations across email for a corporate innovation in that organization, and 6) assessment of sentiment across its email for innovations over time. These examples are discussed in terms of motivations, methods, implications, and applications.

Dynamics and Evolutional Patterns of Social Networks

Previous chapters focused on the models of static networks, which consider a relational network at a given point in time. However, real-world social networks are dynamic in nature; for example, friends of friends become friends. Social network research has, in recent years, paid increasing attention to dynamic and longitudinal network analysis in order to understand network evolution, belief formation, friendship formation, and so on. This chapter focuses mainly on the dynamics and evolutional patterns of social networks. The chapter introduces real-world applications and reviews major theories and models of dynamic network mining.

Detecting Pharmaceutical Spam in Microblog Messages

Microblogs are one of a growing group of social network tools. Twitter is, at present, one of the most popular forums for microblogging in online social networks, and the fastest growing. Fifty million messages flow through servers, computers, and cell phones on a wide variety of topics exchanged daily. With this considerable volume, Twitter is a natural and obvious target for spreading spam via the messages, called tweets. The challenge is how to determine if a tweet is a spam or not, and more specifically a special category advertising pharmaceutical products. The authors look at the essential characteristics of spam tweets and what makes microblogging spam unique from email or other types of spam. They review methods and tools currently available to identify general spam tweets. Finally, this work introduces a new methodology of applying text mining and data mining techniques to generate classifiers that can be used for pharmaceutical spam detection in the context of microblogging.

A Social Network Model for Understanding Technology Use for Knowledge-Intensive Workers

This chapter presents a theoretical model based on social network theories and the social influence model for understanding how knowledge professionals utilise technology. In particular, the association between egocentric network properties (structure, position and tie) and information and communication technology (ICT) use of individuals in knowledge-intensive and geographically dispersed settings is explored. A novel triangulation methodology is adopted where in-depth interviews and observation techniques were utilised to develop constructs for the conceptual model which were then vetted by domain-level experts. A reliable and validated social network-based questionnaire survey is also developed to operationalise the model. Results show that task-level ICT use is significantly associated with degree centrality and functional tie-diversity; and communication-level ICT use is negatively associated with efficiency. The implications of these associations for knowledge-intensive work mean that it is important to consider the professional social network characteristics of potential users of the technology for designing ICT-enabled organisations.

Social Network Inspired Approach to Intelligent Monitoring of Intelligence Data

In the wake of recent terrorist atrocities, intelligence experts have commented that failures in detecting terrorist and criminal activities are not so much due to a lack of data, as they are due to difficulties in relating and interpreting the available intelligence. An intelligent tool for monitoring and interpreting intelligence data will provide a helpful means for intelligence analysts to consider emerging scenarios of plausible threats, thereby offering useful assistance in devising and deploying preventive measures against such possibilities. One of the major problems in need of such attention is detecting false identity that has become the common denominator of all serious crime, especially terrorism. Typical approaches to this problem rely on the similarity measure of textual and other content-based characteristics, which are usually not applicable in the case of deceptive and erroneous description. This barrier may be overcome through link information presented in communication behaviors, financial interactions and social networks. Quantitative link-based similarity measures have proven effective for identifying similar problems in the Internet and publication domains. However, these numerical methods only concentrate on link structures, and fail to achieve accurate and coherent interpretation of the information. Inspired by this observation, the chapter presents a novel qualitative similarity measure that makes use of multiple link properties to refine the underlying similarity estimation process and consequently derive semantic-rich similarity descriptors. The approach is based on order-of-magnitude reasoning. Its performance is empirically evaluated over a terrorism-related dataset, and compared against several state-of-the-art link-based algorithms and other alternative methods.

Semantic-Awareness for a Useful Digital Life

In this book chapter the authors address two main challenges for building compelling social applications. In the first challenge they focus on the user by addressing the issue of building dynamic interaction profiles from the content they produce in a social system. Such profiles are key to find the best person to contact based on an information need. The second challenge presents their vision of “object-centered sociality”, which allows users to create spontaneous communities centered on a digital or physical object. In each case, proof-of-concept industrial prototypes show the potential impact of the concepts on the daily life of users. The main contribution of this chapter is the design of conceptual frameworks for helping users to take maximum advantage from their participation in online communities, either in the digital web ecosystem or real-life spontaneous communities.

Capturing Market Mavens among Advergamers

The objective of this chapter is to identify a market maven segment among advergamers on a mobile-based social networking site (SNS). A real online campaign with a multiplayer game is designed for Procter & Gamble’s Pringles, after which online surveys are conducted via mobile device. Finite mixture models are employed to identify clusters. The estimation results suggest four clusters. The majority group belongs to Clusters 1 (67%) and 2 (21%), while Clusters 3 (6.8%) and 4 (4.8%) exhibit the propensity of market mavens. Specifically, the members of Cluster 3 are likely to have been actively engaged in information search, purchased the sponsor brand, and disseminated their brand knowledge of the brand, mainly through personal conversation after the game play. By contrast, the members of Cluster 4 are unlikely to have sought information, nor to have purchased the brand after the game, but are very likely to have spread their brand knowledge through word-of-mouth. Furthermore, they did so via not only personal conversation but also SNS functions (i.e., messaging, blog, and discussion board). Given this, Clusters 3 and 4 could be labeled as traditional and innovative market mavens, respectively. Our findings suggest that online marketers should identify and incentivize market mavens by branded entertainment so that they can then disseminate information, encourage followers, and generate a viral chain of word-of-mouth.

Social Search and Personalization Through Demographic Filtering

Most existing personalized search systems do not consider group profiling. Group profiling can be an efficient retrieval mechanism, where a user profile is inferred from the profile of the social groups to which the user belongs. The authors propose an XML search system called DemoFilter which employs the concept of group profiling. DemoFilter simplifies the personalization process by pre-defining various categories of social groups and then identifying their preferences. Social groups are characterized based on demographic, ethnic, cultural, religious, age, or other characteristics. DemoFilter can be used for various practical applications, such as Internet or other businesses that market preference-driven products. In the ontology, the preferences of a social group are identified from published studies about the social group. They experimentally evaluate the search effectiveness of DemoFilter and compare it to an existing search engine.

Universal Dynamics on Complex Networks, Really?

The complex network approach developed in statistical physics seems particularly well-suited to analyzing large networks. Progress in the study of complex networks has been made by looking for shared properties and seemingly universal dynamics, thus ignoring the details of networks individual nodes, links, or sub-components. Researchers now need to assess the differences in the processes that take place on complex networks. The author first discusses briefly the theoretical understanding of evolutionary laws governing the emergence of these universal properties (small-world and scale-free networks) and recent evolutions in the field of network analysis. Using data on two empirical networks, a transaction network in the venture capital industry and an interfirm alliance network in a major sector of the biopharmaceutical industry, the author then demonstrates that networks can switch from one ‘universal’ structure to another, but each in its own way. This chapter highlights the need of knowing more about networks, as ‘more is different’.