Article Review: "Classifying Twitter Topic-Networks Using Social Network Analysis"
Timely article for an upcoming paper!
“Classifying Twitter Topic-Networks Using Social Network Analysis,” written by Himeboim, Smith, Rainie, Shneiderman and Espina (2017) serves as a primer on Social Network Analysis (SNA). The article is immensely valuable and provides many takeaways, one being, its clarification on what SNA is (and isn’t). Personally, the inference drawn is how SNA and social media analytics methodically differ and articulating the contradistinction.
This is my first introduction to SNA and graph theory; the transactional nature of digital networks. Also noted is the terminology used to describe SNA work and how it differs from readings such as Baym’s (2007), “Personal Connections in the Digital Age.” Understandably, Baym’s (2017) research focuses on social media from an individual and dyad stance – an interpersonal communication vantage point; while the axis of SNA research illustrates Social Media Network (SNS) structures. The study of “structures” is akin to investigating SNSs from the inter-intra communication and intra-organizational perspectives. Thinking about SNSs from these altitudes is particularly interesting because it takes something I’ve always heavily seen as qualitative (e.g. interactivity and group dynamics) or critical (e.g.: discourse analysis) and quantifies it in deeply visualized, taxonomized, and ontologized ways – an area of study straddling media studies and information science.
When reviewing the article’s “Limitations and Future Studies” section (Himeboim, 2017), it’s refreshing to see that the methods can be extended beyond the Twitter universe and applied to other social media (e.g. blogs, discussion boards, etc.). Given high scholarly interest SNSs, this flexibility offers scholars the room to examine networks outside of the typical Facebook and Twitter realm (Borah, 2015). Furthermore, Himeboim et at. (2017) echos Ellison and boyd’s (2013) forewarning on the fast-pace of technological change and its impact on research replication.
There are two SNSs that are exceptionally interesting to me – primarily YouTube, and secondarily LinkedIn. As stated in Himeboim et al. (2017), the SNA concepts and methods have been applied to YouTube research and successfully published by Xu, Park, and Park (2015). “Networked Cultural Diffusion and Creation on YouTube: An Analysis of YouTube Memes” (Xu, Park and Park, 2016) is now on my reading list. Now, LinkedIn seems to be a different story. In comparison to Facebook and Twitter, scholarly research on LinkedIn is shallow in quantity. Naturally, a large body zeros in on the business-orientation of the site and its implications on recruiting efforts, user demographics, and professional development. A notable exception is Papacharissi (2009), “The Virtual Geographies of Social Networks: A Comparative Analysis of Facebook, LinkedIn and ASmallWorld” – but like all studies research topical-coverage and limitations exist.
Compounding matters, there is a gap in the number of analytical tools that support data mining and social media analysis for LinkedIn. Leading tools such as Crimson Hexagon, Sprout, and Hootsuite currently do not offer LinkedIn examination at acceptable levels for most in-depth scholarly inquiries. Interestingly, LinkedIn has built its own tools which are primarily marketed to business professional, corporations, and individuals – leaving the academic community much to desire. Two potential exceptions are SociLab and Gephi, tools I yet to holistically review but look promising on the surface – further exploration is needed. The underlying question is whether these tools allow for SNA beyond one’s individual connections.
Adamantly, I will continue to search for relevant LinkedIn research. The scholarly field, in general, has vast and wide interest and output on SNS research. Therefore, I am confident that I will eventually find what I’m looking for.
References
Borah, P. (2015). Emerging communication technology research: Theoretical and methodological variables in the last 16 years and future directions
Ellison NB and boyd d (2013) Sociality through social network sites. In: Dutton WH (ed.) The
Oxford Handbook of Internet Studies. Oxford: Oxford University Press, pp. 151–172.
Itai Himelboim, Marc A. Smith, Lee Rainie, Ben Shneiderman and Camila Espina (2017). Classifying Twitter topic-networks using social network analysis. Social Media + Society. 1 –13. http://journals.sagepub.com/doi/full/10.1177/2056305117691545
Ouyang, F. and Reilly, C. (2015). Photograph. Retrieved from https://sites.google.com/a/umn.edu/social- network-analysis/.
Papacharissi, Z. (2009). The virtual geographies of social networks: A comparative analysis of Facebook, LinkedIn and ASmallWorld. New Media & Society, Vol. 11, No. 1-2, pp. 199- 220, doi:10.1177/1461444808099577
Xu, W. W., Park, J. Y., Kim, J. Y., Park, H. W. (2016). Networked cultural diffusion and creation on YouTube: An analysis of YouTube memes. Journal of Broadcasting & Electronic Media, 60, 104–122.
