Collecting vertical trace data: Big possibilities and big challenges for multi-method research

Abstract

Every person who sends email, text messages, tweets, or simply surfs the Web leaves a digital trace. Researchers are just starting to comprehend the possibilities of “big data” for creating a new picture of social behavior. The potential for innovative work on social and cultural topics far outstrips current data collection and analysis techniques for a variety of reasons, including researchers' lack of access to corporate data sets, technical skills, and analytical lenses. This article draws a distinction between “horizontal” trace data sets that aggregate a specific type of trace, such as all tweets with a certain hashtag, and “vertical” trace data sets, which are organized around research participants. Although both types of data are observations of real‐world digital behavior, they each have distinct advantages and disadvantages for the researcher. Combining these two forms of data provides a richer picture of online activity, and adding other types of data such as interviews, surveys, and experiments can contextualize online activity within broader social processes.

Reimagining Internet Geographies: A User-Centric Ethnological Mapping of the World Wide Web

“…Another type of popular WWW imageries is based on the coverage of usually US‐based IT giants such as Facebook and Twitter, which veils the political and economic specificities of the particular online applications (see Menchen‐Trevino, ).…”

“…This is consistent with the use of the term in the literature (e.g., DeNardis, 2012;Dodge, 2008). 2 Another type of popular WWW imageries is based on the coverage of usually US-based IT giants such as Facebook and Twitter, which veils the political and economic specificities of the particular online applications (see Menchen-Trevino, 2013). 3 Additionally, hyperlinks may contain erroneous links, irrelevant information, and inconsequential relationships (Weber & Monge, 2011).…”

Open-Source’s Inspirations for Computational Social Science: Lessons from a Failed Analysis

“…Perhaps I should have added surveys to the digital data, as some researchers have suggested (Stier, Breuer, Siegers, & Thorson, 2019). Perhaps I was too narrow in my focus, looking only at the fans or users as they participated on Reddit but missing their activity on other platforms (Menchen-Trevino, 2013). Researchers have noted how Skyrim (Bethesda Softworks, 2011) fans (Puente & Tosca, 2013) and people in general (Baym, 2007) use more than one online space for their online activity.…”

“…The advanced questions we can ask now, building on decades of research and methods, call for advanced methods and understanding. Advanced methods require more than just large amounts of raw computing power; they call for both quantitative and qualitative methodological approaches (Menchen-Trevino, 2013), a broad understanding of theories, topical expertise, and computational skills. In short, CSS done well requires teams (Lazer et al, 2009).…”

Analyzing the Effect of Time in Migration Measurement Using Georeferenced Digital Trace Data

“…Among the prom is ing new sources are dig i tal trace data (Deville et al 2014;FriasMartinez et al 2012;Hawelka et al 2014). Generated as a byproduct of ev ery day in for ma tion tech nol ogy use, dig i tal trace data con sist of in di vid u allevel re cords of dig i tal be hav ior, which may in clude in for ma tion on a per son's phys i cal lo ca tion (MenchenTrevino 2013). With the global pro lif er a tion of dig i tal tech nol o gy, dig i tal trace data are in creas ingly com mon and are avail able in a wide range of forms that are po ten tially use ful to mi gra tion schol ars, such as metadata as so ci ated with cel lu lar calls and texts, GPS in for ma tion cap tured pas sively by smartphone ap pli ca tions, and geotags posted to so cial me dia or other lo ca tionbased so cial net works (LBSNs) (Girardin et al 2008).…”