Social Network Analysis: Considering Alternative Methodologies
The increasing extent of the Internet has also witnessed an incredible increase in the popularity of online social networking. Websites such as ‘Facebook‘ and ‘Twitter‘ are now the most frequently referred to examples although there are countless other social networking platforms that exist today. With a web phenomenon reaching such extremely large user bases as these sites have, it is critical that researchers take a closer look at how these platforms function and what impacts they actually have. Such ‘network analysis’ has been extensively carried out, often times with mathematical and graphical methods because such methods allow for the use of computers that allow for greater computational power in general but also greater accuracy and speed. ‘Graph theory‘ is extensively applied in the research of online social networks and graphical representations often allow researchers to visualize results in clear ways that allow for the recognition of particular trends or patterns that may be overlooked without such visuals.
With the use of mathematics and graphical methods of research, researchers often focus particularly on ‘actors’ within such networks i.e. ‘users’ and their ties, i.e. the connections these users have with other users and other ‘nodes‘ within the network. Importantly, such research investigates the ‘connectivity‘ of these actors by measuring elements such as ‘frequency’ of connections pertaining to the amount of interaction between two such actors, and the connectivity between various ‘nodes’ in the network. On the basis of such results, researchers are able to map such an online social network revealing trends that characterize both the platform and the ways in which the ‘actors’ or ‘users’ interact with such a platform. Such research then is primarily focused on aspects such as ‘organization’ and ‘connectivity’ of nodes and factors such as, ‘size’ and ‘density’ of connections. Such approaches to network analysis have become the most common angle from which to research such digital networks.
With researchers applying these methods there has been a significant amount of ‘data-mining‘ taking place in relation to the gathering of such data, especially considering the datasets are often extremely large, also leading back to the necessity of using computers in order to manage such large datasets. However, in addition to primarily employing such mathematical techniques and the implementation of graph theory, I believe it would be interesting to combine such research methodologies with alternative more interactive techniques that focus on the participation of the ‘user’. It is fairly common for such platforms to employ techniques such as surveys and polls, however many times these are intended for gathering data for purposes of improving the platform and many times users don’t participate.
In order to make such methods useful for researchers analyzing such a network it may be a consideration to put forth such polls and surveys as a ‘pre-condition’ to participating in such a network. Just as users are required to fill in certain information to identify themselves and an email address to confirm legitimacy in addition to agreeing with ‘user terms’, such polls or surveys may be introduced as another requirement which may then be conducted at regular intervals and kept short and easy. Although the legitimacy of the answers of such methods is also questionable, several categories may be designed so that they may be cross-referenced for authenticity and in such a manner be combined with the mathematical, statistical and graphical methodologies. ‘Polls’ for example are often short and to the point and users may generally partake if given an incentive to participation. Such methods may be implemented to encourage users to collectively contribute to the collection of such datasets for research.
From another perspective, it may be really interesting to implement other media forms in the gathering of research data in order to investigate elements other than traditional actor-node relationships with a focus on connections. As the boundary between nodes and actors blurs within an increasingly ‘distributed‘ internet and the immense increase in mobile technologies that support the use of such online social networks, researchers may look more closely at research methodologies that specifically target such mobile devices.
The concept of ‘geodesic’ distances is common when researching networks and may be implemented further to include data sent and received from mobile devices. Considering the ‘geodesic’ path is the ‘easiest’ or most logical path chosen to transfer information, the distinction between users making use of their mobile devices versus their home computer is something worth looking at. Although a user may be relatively close to their home computer or even using it at the time, I have seen such users grabbing their mobile phone instead in order to access a social network. I believe this is a phenomena worth looking into considering it is sometimes quite illogical to see such users of online social networks diverging from such geodesic paths.
Furthermore, I believe it would be very interesting to research the effect such ‘mobility’ has on the nature of postings of such users. This may also in part explain users grabbing their mobile phone because they may easily take a picture for example with their mobile device (which nowadays nearly all have cameras) and post the picture instantly. It is also through research of such applications that enable social networks to be accessed on mobile phones that demographic data may be looked at more closely. Although this again raises concerns of ‘privacy’, which was already a common problem within social networks, the activities of such users of mobile applications may provide insight into the connections between such activities, locations and the nature of the resulting postings.
