Finding Automated (Bot, Sensor) or Semi-Automated (Cyborg) Social Media Accounts Using Network Analysis and NodeXL Basic

Various research findings suggest that humans often mistake social robot (‘bot) accounts for human in a microblogging context. The core research question here asks whether the use of social network analysis may help identify whether a social media account is fully automated, semi-automated, or fully human (embodied personhood)—in the contexts of Twitter and Wikipedia. Three hypotheses are considered: that automated social media account networks will have less diversity and less heterophily; that automated social media accounts will tend to have a botnet social structure, and that cyborg accounts will have select features of human- and robot- social media accounts. The findings suggest limited ability to differentiate the levels of automation in a social media account based solely on social network analysis alone in the face of a determined and semi-sophisticated adversary given the ease of network account sock-puppetry but does suggest some effective detection approaches in combination with other information streams.

Employing the Sentiment Analysis Tool in NVivo 11 Plus on Social Media Data

Sentiment analysis has been used to assess people's feelings, attitudes, and beliefs, ranging from positive to negative, on a variety of phenomena. Several new autocoding features in NVivo 11 Plus enable the capturing of sentiment analysis and extraction of themes from text datasets. This chapter describes eight scenarios in which these tools may be applied to social media data, to (1) profile egos and entities, (2) analyze groups, (3) explore metadata for latent public conceptualizations, (4) examine trending public issues, (5) delve into public concepts, (6) observe public events, (7) analyze brand reputation, and (8) inspect text corpora for emergent insights.

Social Media in Knowledge Management

This chapter discusses in detail about Knowledge Management and how Social Media tools and platforms can be used for Knowledge Management and how they can be integrated into Knowledge Management system. This chapter explains the key aspects of Knowledge Management and Social Media and how Social media can be used to capture both tacit and explicit knowledge and also to share knowledge among the communities of practice both within organizations and also outside the organizations. The chapter provides an overview of using social media to enhance knowledge management and collaboration in a corporate context and gives an insight on how firms get the most value from social media tools like wikis, blogs, microblogging, social tagging and some such similar tools in Knowledge Management. Further research directions based on the review of the literature are proposed.

Social Media

Rapid changes are taking place in global business scenario. It has become a necessity for enterprises to adapt to these changes. Social media facilitates business enterprises to make use of the opportunities in the global market. Social media has become an important source of information for the stakeholders of business enterprises. Structured, semi-structured, and unstructured data from social media provide a good scope for developing business models for enterprises. This chapter mainly talks about developing the conceptual business models in the sectors such as automobiles, textiles and software developing companies. Further, this chapter explains making use of the concepts such as virtual reality, multimedia and cloud computing with the data from social media in developing conceptual business models.

Social Media Metrics in an Academic Setup

Social media is perhaps responsible for largest share of traffic on the Internet. It is one of the largest online activities with people from all over the globe making its use for some sort of activity. The behaviour of these networks, important actors and groups and the way individual actors influence an idea or activity on these networks, etc. can be measured using social network analysis metrics. These metrics can be as simple as number of likes on Facebook or number of views on YouTube or as complex as clustering co-efficient which determines future collaborations on the basis of present status of the network. This chapter explores and discusses various social network metrics which can be used to analyse and explain important questions related to different types of networks. It also tries to explain the basic mathematics behind the working of these metrics. The use of these metrics for analysis of collaboration networks in an academic setup has been explored and results presented. A new metric called “Average Degree of Collaboration” has been defined to quantify collaborations within institutions.

Exploring Public Perceptions of Native-Born American Emigration Abroad and Renunciation of American Citizenship through Social Media

There has been little work done on American emigration abroad and even less done on the formal renunciation of American citizenship. This chapter provides an overview of both phenomena in the research literature and then provides some methods for using the extraction of social media data and their visualization as a way of tapping into the public mindsets about these social phenomena. The software tools used include the following: Network Overview, Discovery and Exploration for Excel (NodeXL Basic), NVivo, and Maltego Carbon; the social media platforms used include the following: Wikipedia, YouTube, Twitter, and Flickr.

Big Data in Social Media Environment

Information technology has reached its pinnacle, with the era being dominated by two hi-tech driving forces - Big data and Social media. Big data encompasses a wide array of data mining workloads, extracted through various sources, the results of which are of keen interest to business leaders and analysts across every industry segment. Data from the social media is exploding at an exponential rate and is being hailed as the key, to crucial insights into human behavior. Extracting intelligent information from such immense volume, variety and velocity of data, in context to the business requirement is the need of the hour. Therefore, new tools and methods specialized for big data analytics is crucial, along with the architectures for managing and processing such data. Big data complemented with Social Media offers a new horizon to take management practice to an advanced level.

Productivity on the Social Web

Companies gain competitive advantage when they are in a better position than its competitors to keep customers, so for providing the greatest value, become a captivating option, generate satisfaction and achieve the loyalty of consumers, it is necessary that they know the market and enter into a profitable relationship with the customer. In order to get closer to the public, the social media presence stands as a very attractive option for the companies, but these wonder if the effort will offset the result obtained. In this chapter, we will define the concept of enterprise 2.0, and will explain the main benefits that a company can get with the adoption of social media, in relation to its brand image and reputation, communication with the public and the increase of traffic that it can get to the corporate website. Then, and after pointing out the most popular social software tools, we will focus on social media metrics, defining the different types of metrics, designing a framework of social analysis and highlighting those that prove to be of greater business value.

Transformation of Business through Social Media

Social media are widely used in regular operations of many companies, including start-ups, small, medium and large organizations. The Social media are fundamentally changing the way we communicate, consume, collaborate and create. It creates one of the most transformative impacts on business. The most significant consequence of social media has been the shift of power from the institution to the individual. These shifts in the consumer-brand relationship have thrown up new challenges and opportunities for business organization. Social media have transformed the ways businesses from marketing and operations to finance and human resource management. Increasingly, social media are also transforming the way businesses relate to workers, allowing them to build flexible relationships with remote talent, to crowdsource new ideas, or to engage in micro outsourcing. Social media are increasingly being used in organizations to improve relationships among employees and nurture collaboration and the sharing culture. The purpose of this research is to explore the major changes which have taken place in organization because of social media.

Capturing the Gist(s) of Image Sets Associated with Chinese Cities through Related Tags Networks on Flickr®

To introduce how related tags networks may be extracted from Flickr® and used for “gist” and other analysis, this chapter describes the related tag networks associated with some of the cities of the People's Republic of China (used as seeding terms). The software used for the data extractions (from the Flickr® API) and the creation of various graph visualizations is the free and open-source Network Overview, Discovery and Exploration for Excel (NodeXL Basic), available on Microsoft's CodePlex platform.