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Collecting vertical trace data: Big possibilities and big challenges for multi‐method research
Author(s) -
MenchenTrevino Ericka
Publication year - 2013
Publication title -
policy and internet
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.281
H-Index - 26
ISSN - 1944-2866
DOI - 10.1002/1944-2866.poi336
Subject(s) - trace (psycholinguistics) , big data , computer science , variety (cybernetics) , data science , world wide web , data collection , data type , aggregate (composite) , information retrieval , sociology , data mining , artificial intelligence , social science , philosophy , linguistics , materials science , composite material , programming language
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 .