Code Reuse

The reuse of code can be used to add or update functionalities with little or no modifications to new or existing software applications. Developers have reused sections of code when the code is available but have been hindered by finding the code that is needed for an application. By creating a code repository, code would be available to developers in a systemic method. The code would be available for functional and nonfunctional uses in applications. Since the code has already be written, during the discovery phase of projects the developers involved should be able to search the repository for the code that is needed for strategies and problems that have already been successfully been implemented. Quality, cost, and time should be the focus of code reuse. To maximize code reuse, a code repository that is properly categorized and indexed would add to the software development lifecycle by making code available to developers that they can use with confidence. The code repository will improve the application process.

Creating “(Social) Network Art” with NodeXL

If human-created objects of art are historically contingent, then the emergence of (social) network art may be seen as a product of several trends: the broad self-expression and social sharing on Web 2.0; the application of network analysis and data visualization to understand big data, and an appreciation for online machine art. Social network art is a form of cyborg art: it melds data from both humans and machines; the sensibilities of humans and machines; and the pleasures and interests of people. This chapter will highlight some of the types of (social) network art that may be created with Network Overview, Discovery and Exploration for Excel (NodeXL Basic) and provide an overview of the process. The network graph artwork presented here were all built from datasets extracted from popular social media platforms (Twitter, Flickr, YouTube, Wikipedia, and others). This chapter proposes some early aesthetics for this type of electronic artwork.

Flickering Emotions

Using the emotion words of Robert Plutchik's “Wheel of Emotions” (based on his multidimensional emotion model) as seeding terms to extract related tags networks (and related thumbnail imagery) from Flickr (at 1 deg., 1.5 deg., and 2 deg.), it is possible to formulate (1) insights about emotions and their interrelationships (through the lens of collective folksonomic tagging), (2) understandings about what the related tags in the networks may suggest about the image item holdings on Flickr, and (3) awareness of the collective mental models of the Flickr users regarding particular emotions, and (4) fresh methods of research to folk tagging through the extraction and analysis of related tags networks and related thumbnail imagery. This chapter introduces this case of analyzing related tags networks to more deeply understand public conceptualizations of emotions through data labels.

Weak Ties and Value of a Network in the New Internet Economy

The rapid rise in internet economy is reflected in increased scholarly attention on the topic, with researchers increasingly exploring the marketing approaches and strategies now available through social media. The network provides a value for companies, thus becomes essential acquire greater awareness to evaluate and quantify its value. What are practical implications for managers? Social network analysis is nowadays an essential tool for researchers: the aim of this chapter is to extend the internet economy research to network theories. Today, there are emerging observations on the global internet economy, but there is a big gap in literature indeed. At first, literature focused on people. Now, on digitalized information. Firms are connected in a virtual network and there are undefined distances in terms of space and time. Traditional methods of analysis are no more efficient: to analyze the relationship in the network society, we need a different paradigm to approach network issue.

Facebook Content Analysis

Understanding the motives that encourage users to adopt social media to communicate with businesses is very important. This research study was conducted with Australian banks and adds to the development of empirically tested social media adoption model consisting of technological and social communication aspects (Senadheera, 2015). This chapter presents the findings of the research study based on analysis of wall posts gathered from Australian banks' Facebook presence in the year 2013. The research study involves a thematic analysis of frequently used words by Australian banks in their respective Facebook wall posts following an outcome of a word frequency test conducted using NVivo. This analysis was conducted with the proposed adoption model as the basis to determine whether banks' Facebook content addresses the basic user requirements driving them to adopt social media to communicate with Australian banks. The results strengthens the robustness and the applicability of the social media adoption model.

Exploring “User,” “Video,” and (Pseudo) Multi-Mode Networks on YouTube with NodeXL

Network Overview, Discovery and Exploration for Excel (NodeXL Basic) enables the extraction of “user” (entity), “video” (content), and pseudo multi-modal networks from YouTube. This open-source add-on to NodeXL captures a wide range of data, enables data processing for analysis, and then visualization in a variety of graphs (based on different layout algorithms). This chapter summarizes some of the “askable” questions using this approach. Various types of data extractions are shared to give a sense of the breadth of some approaches, including the following: (1) entities, (2) in-world phenomena, (3) imaginary phenomena, (4) themes, (5) reputations by name, (6) genres, (7) language-specific phenomena, and (8) location-specific phenomena.

Usability Evaluation of Social Media Web Sites and Applications via Eye-Tracking Method

This chapter describes usability studies of website-based and mobile application-based social media sites. In the study including 10 university students, the completion time of assigned tasks were measured along with click numbers and completion situations. These measures were analyzed. Data obtained from eye tracking movements was analyzed, and the results were evaluated. According to the results, the users can complete most of the tasks, but completion time varied. The participants had difficulties completing settings menu tasks except menus previously used in social media. When eye tracking results were examined, it was revealed that they mostly focused on the left side of websites and mobile applications. The participants stated that mobile applications were more useful than websites. According to eye-tracking data obtained in the study and the users' opinions, mobile social media applications were more functional than their websites.

Customer Complaints in Social Networks in the Spanish Telecommunication Industry

Nowadays social networks have a high potential to disseminate information, positive and negative, of any person, organization or product generating electronic word of mouth through customers' comments and complaints. Consequently, this paper proposes a novel research on the content related to the users' online complaints and seeks to understand the power of social networks as creating electronic word of mouth. For this task, the user-generated social network Critizen is used, especially critiques related to one of the most criticised sectors in Spain, that is, the telecommunication industry. The main results of this paper reveal that content analysis is an effective technique to extract business value from the vast amount of data available on the Internet, and especially in social networks.

A Route Recommender System Based on Current and Historical Crowdsourcing

Due to the rise of the social networks it's possible to use techniques based on crowdsourcing to easily gather real-time information directly from citizens in order to create recommendation systems capable to employ knowledge that is shared from the crowd. Particularly, in Twitter, the users publish a big amount of short messages; however, to automatically extract useful information from Twitter is a complex task. In order to provide an informed recommendation of the current best route between two city points, this chapter introduces a workflow that integrates natural language techniques to build an vector of features for training two linear classifiers which obtain current information from Twitter, and integrates that information with historical information about possible routes using exponential smoothing; current and historical data to feed a route selection algorithm based on Dijkstra. The effectiveness of the proposed workflow is shown with routes between two interest points in Caracas (Venezuela).

Mining the Edublogosphere to Enhance Teacher Professional Development

The rapid proliferation of Web 2.0 technologies during the last decade has led to online communities of learners, where members share prerequisite knowledge and skills to further professional growth. The educational affordances of blogs offer a wealth of online resources that can be mined in order to gain better understanding of community members and their contributions. In this chapter, we outline a web content mining technique to extract and analyze information from educational blogs, resulting in networks of online resources where content is linked in terms of the underlying semantic relationships. This type of representation is referred to as a network-based model, and has implications for e-learning personalization systems that seek to suggest similar content to those preferred by other learners. The applications of this method are discussed in terms of enhancing teacher professional development in the context of nBrowser, an intelligent web browser designed to support pre-service teachers in building lesson plans that integrate technologies in the classroom.