The following article notes some considerations of social research within digital environments.
Online mediums have brought with them different possibilities for social research, particularly providing new opportunities for quantification. Brought about by changes in both the scale and topology of network interactions, the tools available and nature of research possible, research landscapes are changing. Social sciences have seen push to become more empirical though these approaches, whilst computational sciences have seen a softening of theirs and a more natural embracement of imprecision particularly in systems for social settings. The following article notes some considerations of social research within digital environments. This are predominantly unstructured notes on ideas, the methods page in the tool bar serves as a repository for some specific approaches of interest.
Methods page: Repository of some collected methods
Methods for conducting social research online must evolve their sensibilities to match the technologies they are inquiring. Richard Rodgers director of the Digital Methods Initiative notes the turn in research seen in the transition from web 1.0 to web 2.0. Whereas research in the web 1.0 era mostly involved scrappers and link analysis, web 2.0 research has produced predominantly API based research centred around the dominant platforms (2018. p.93-94). This periodised trend is reflective of wider user migrations from an open information network, to more centralised social networks.
The affordances of social media sites each configure user’s capacities for action differently. (Bucher, T. and. Helmond, A. 2018) likewise: ‘Platforms don’t just mediate public discourse, they constitute it”’ (Gillespie, T. 2018)
Social interaction online takes place principally in automated environments where human and non-human agency is an active state of interplay. The dynamics and capacity for non-human influence varies from site to site. Facebook for instance has markedly more algorithmic curation when compared to Twitter. Twitter though providing more control over content curation makes it easier to create bots. A recent study argued something like 9-15% of all tweets may come from automated accounts.
Random: x% of the total population selected randomly.
Snowball: Iteratively build sample from developing connections from initial set. Network/graph based.
Topic-based: Filter for specific conditions (Keywords, users, hashtags).
Marker-based: Filter for specific meta-data such as location, language.
- Gerlitz, C. and Rieder, B. 2013. Mining One Percent of Twitter: Collections, Baselines, Sampling. M/C Journal. [Online]. 16(2). [Accessed 17 March 2018]. Available from: http://journal.media-culture.org.au/index.php/mcjournal/article/view/620
- Rogers, R. 2018. Digital methods for cross-platform analysis. In: Burgess, J., Marwick, A. and Poell, T. ed. The SAGE Handbook of Social Media. London: SAGE Publications. pp.91-110.
- Bucher, T. and. Helmond, A. 2018. The Affordances of Social Media Platforms. In: Burgess, J., Marwick, A. and Poell, T. ed. The SAGE Handbook of Social Media. London: SAGE Publications. pp.233-253.
- Boyd, D. and Crawford, K. 2012. Critical questions for big data. Information, Communication & Society. [Online]. 15(5). pp.662-679. [Accessed 10 April, 2018]. Available from: https://doi.org/10.1080/1369118X.2012.678878
- Neff, G. and Nagy, P. 2016. Automation, Algorithms, and Politics: Symbiotic Agency and the Case of Tay. International Journal of Communication. [Online]. 10(1), 4915–4931. [Accessed 10 November 2016] Available from: http://ijoc.org/index.php/ijoc/article/view/6277