
Open access
Date
2018-12-21Type
- Journal Article
Citations
Cited 13 times in
Web of Science
Cited 15 times in
Scopus
ETH Bibliography
yes
Altmetrics
Abstract
Information forms the basis for all human behavior, including the ubiquitous decision-making that people constantly perform in their every day lives. It is thus the mission of researchers to understand how humans process information to reach decisions. In order to facilitate this task, this work proposes LASSO regularization as a statistical tool to extract decisive words from textual content in order to study the reception of granular expressions in natural language. This differs from the usual use of the LASSO as a predictive model and, instead, yields highly interpretable statistical inferences between the occurrences of words and an outcome variable. Accordingly, the method suggests direct implications for the social sciences: it serves as a statistical procedure for generating domain-specific dictionaries as opposed to frequently employed heuristics. In addition, researchers can now identify text segments and word choices that are statistically decisive to authors or readers and, based on this knowledge, test hypotheses from behavioral research. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000309052Publication status
publishedExternal links
Journal / series
PLoS ONEVolume
Pages / Article No.
Publisher
Public Library of ScienceOrganisational unit
09623 - Feuerriegel, Stefan (ehemalig) / Feuerriegel, Stefan (former)
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Show all metadata
Citations
Cited 13 times in
Web of Science
Cited 15 times in
Scopus
ETH Bibliography
yes
Altmetrics