Multimodal Mapping of a University's Formal and Informal Online Brand: Using NodeXL to Extract Social Network Data in Tweets, Digital Contents, and Relational Ties

Multimodal Mapping of a University's Formal and Informal Online Brand: Using NodeXL to Extract Social Network Data in Tweets, Digital Contents, and Relational Ties

Copyright: © 2018 |Pages: 53
DOI: 10.4018/978-1-5225-5637-4.ch052
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With the popularization of the Social Web (or Read-Write Web) and millions of participants in these interactive spaces, institutions of higher education have found it necessary to create online presences to promote their university brands, presence, and reputation. An important aspect of that engagement involves being aware of how their brand is represented informally (and formally) on social media platforms. Universities have traditionally maintained thin channels of formalized communications through official media channels, but in this participatory new media age, the user-generated contents and communications are created independent of the formal public relations offices. The university brand is evolving independently of official controls. Ex-post interventions to protect university reputation and brand may be too little, too late, and much of the contents are beyond the purview of the formal university. Various offices and clubs have institutional accounts on Facebook as well as wide representation of their faculty, staff, administrators, and students online. There are various microblogging accounts on Twitter. Various photo and video contents related to the institution may be found on photo- and video-sharing sites, like Flickr, and there are video channels on YouTube. All this digital content is widely available and may serve as points-of-contact for the close-in to more distal stakeholders and publics related to the institution. A recently available open-source tool enhances the capability for crawling (extracting data) these various social media platforms (through their Application Programming Interfaces or “APIs”) and enables the capture, analysis, and social network visualization of broadly available public information. Further, this tool enables the analysis of previously hidden information. This chapter introduces the application of Network Overview, Discovery and Exploration for Excel (NodeXL) to the empirical and multimodal analysis of a university's electronic presence on various social media platforms and offers some initial ideas for the analytical value of such an approach.
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A Review Of The Literature

To offer a brief overview: research into social networks started in the early 20th century within sociology. Researchers started to create quantitative measures of network relationships in the 1930s with sociometry. Psychologists contributed to this work in the 1940s by formally defining cliques (or subnetworks). In the 1950s and 1960s, anthropologists started applying social network analysis to their work. Some researchers integrated elements of game theory and economics into their social network analyses—for a highly multi-disciplinary approach. Various researchers have since contributed rich research and theorizing about social networks. In the 2000s, the computing sciences offered ways to analyze and visualize social networks.

To contextualize, there are some basic governing logics and underlying assumptions. One central concept is that much of human endeavor occurs in social groupings and fairly stable human relationships (defined by social roles, bureaucratic structures, social practices, and others). Human groups tend to be fairly hierarchical, and those with power accrue much more than others in terms of decision-making, power, information, and resources. They are theorized to have a clearer sense of a social network than others who are relatively less privileged in terms of positions. Human connections matter. People do not connect randomly, but they tend to build relationships in homophilous ways, with similarity being attracted to similarity (or “preferential attachment”). On some work teams, heterophily (the like of differences) is preferred to ensure a diverse skill set and variant perspectives. Social network analysis involves the analysis of the structural aspects of such connections. The proximity of an individual’s nodes suggests a direct influence; network analysis shows that an influential node may have distal influence as well (influence at a distance). The structure of a network may shed light on its capabilities.

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