This paper reviews Deen Freelon's 2014 article "On the Interpretation of Digital Trace Data in Communication and Social Computing Research," published in the Journal of Broadcasting & Electronic Media. The review examines the article's core argument that individuals leave activity traces when interacting with digital devices, and that these traces can support personal reflection and social reminiscence. It summarizes the study's prior research base, key findings regarding vertical and horizontal trace data sets, the ethnographic methods employed, and the author's recommendations for integrating qualitative techniques into computational social science. The review also notes the article's treatment of statistical representativeness and ethical concerns surrounding social media data.
The purpose of Freelon's (2014) article is to enhance the design and research in social media networks by better developing technological actions — such as interface design, behavior modeling, and system building — with an understanding of social characteristics as indicated by social technology concepts. In particular, the author seeks to show that individuals leave traces of their activities when interacting with digital devices. This observation foreshadows the present research and practical interest in big data. Most of the work revolves around how to derive significance from these traces (Freelon, 2014).
The prior research on which the study is based examined social uses of trace data and the failure to adapt social reminiscing. Researchers found that choices around consuming, creating, and delegating data in social media platforms are complex. This is because individuals simultaneously manage concerns around relationship dynamics, identity management, and the visibility of their activity to themselves and to third parties.
The article supports individual reflection and reminiscence, which are key psychological processes for all age groups. A central insight the article presents is that data people accumulate in social media networks can support personal reflection and social reminiscence.
The article is significant because it draws a distinction between two forms of big trace data sets: vertical and horizontal. This distinction helps in addressing practical and conceptual challenges confronted by computational social science and expands the toolkit available to digital ethnographers. Ethnographic researchers rely on participant observation and use trace data sets as observational data. Thus, both the vertical and horizontal data analysis presented in the article are crucial for understanding digital traces in a more comprehensive way (Freelon, 2014).
"Participation traces and ethnographic observation methods"
"Future qualitative integration and author evaluation"
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