@inproceedings{3d4f7272e1884d969e65316276f64562,
title = "Uncovering latent archetypes from digital trace sequences: An analytical method and empirical example",
abstract = "The widespread availability of digital trace data provides new opportunities for researchers to understand human behaviors at a large scale. Sequences of behavior, captured when individuals interface with an information system, can be analyzed to uncover behavioral trends and tendencies. Rather than assume homogeneity among actors, in this study we introduce a method for identifying subsets of the population which demonstrate similar behavioral trends. The objective of this analysis would be to identify a finite set of behavioral archetypes, which we define as distinct patterns of action displayed by unique subsets of a population. This study makes a contribution to the literature by introducing a novel methodology for analyzing sequences of digital traces. We apply our technique to data from a lab experiment featuring thirty twenty-person teams communicating over Skype.",
keywords = "Digital traces, Sequence analysis, Social networks, Unsupervised methods",
author = "Aaron Schecter and Noshir Contractor",
note = "Publisher Copyright: {\textcopyright} 40th International Conference on Information Systems, ICIS 2019. All rights reserved.; 40th International Conference on Information Systems, ICIS 2019 ; Conference date: 15-12-2019 Through 18-12-2019",
year = "2019",
language = "English (US)",
series = "40th International Conference on Information Systems, ICIS 2019",
publisher = "Association for Information Systems",
booktitle = "40th International Conference on Information Systems, ICIS 2019",
address = "United States",
}