Читать книгу Innovations in Digital Research Methods - Группа авторов - Страница 25

The distinctive contribution of social network analysis (SNA) to social research is its stress on the importance of studying the structure of relationships between people rather than considering them as unconnected individuals. Like many of the other advances in research methods covered in this book, SNA is a mature methodological tool. Arguably, it owes its rise to greater prominence in recent years to two factors. One is that, as with many other established social research methodologies, e-Infrastructure has extended the scale and complexity of what is achievable, in this case by providing SNA with new and more powerful means to capture social network datasets, analyse them and visualize the results. The second factor is that many of the new types and sources of digital social data – such as hyperlink networks (the structures of links between websites) and social networking sites such as Facebook and Twitter – are inherently relational.

In this chapter, Ackland and Zhu review the history and methodological principles of SNA, and survey several of the research tools now available for SNA data collection, analysis and visualization. They draw on examples of studies of Facebook, Twitter, Flickr, online newsgroups and websites to illustrate contemporary and arguably the most prominent uses of SNA – to study people’s behaviour in social networking sites. Ackland and Zhu go on to discuss two key ontological questions associated with SNA as a research methodology. The first is its ‘construct validity’, an issue that has potentially major implications. Simply put, the question is: do the social structures observed in, for example, Facebook, have real-world analogies or are they properties only of the online world, entirely unrelated to its real world counterpart? If the answer is no, then arguably, for all the talk about the opportunities for social research offered by new sources of social data, the impact in terms of increased understanding of social phenomena will be very limited.

Ackland and Zhu’s second question relates to debates about the capacity of social research methodologies to distinguish between causality and correlation. Here, they offer a somewhat more optimistic prognosis, observing that data generated through people’s activity on, for example, social networking sites, is rich and time-stamped, allowing for more fine-grained analysis, while the sites themselves can be thought of as natural research instruments, ideal for carrying out large scale experiments.⁴ Like other contributors to this volume, they conclude with a warning about the pitfalls for researchers of relying on data sources, such as Facebook, that are proprietary and whose access is subject to terms and conditions that may change at any time.