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What we tweet about when we tweet about taxes: a topic modelling approach
Puklavec, Žiga
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Kogler, Christoph
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Stavrova, Olga
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Zeelenberg, Marcel
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1-s2.0-S016726812300238X-main.pdf
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DOI:
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https://doi.org/10.1016/j.jebo.2023.07.005
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URL:
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https://www.sciencedirect.com/science/article/pii/...
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URN:
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urn:nbn:de:bsz:180-madoc-715049
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Dokumenttyp:
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Zeitschriftenartikel
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Erscheinungsjahr:
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2023
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Titel einer Zeitschrift oder einer Reihe:
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Journal of Economic Behavior & Organization : JEBO
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Band/Volume:
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212
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Heft/Issue:
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Special issue: The shadow economy, tax behaviour and institutions
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Seitenbereich:
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1242-1254
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Ort der Veröffentlichung:
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Amsterdam [u.a.]
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Verlag:
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Elsevier
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ISSN:
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0167-2681 , 1879-1751
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Sprache der Veröffentlichung:
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Englisch
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Einrichtung:
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Fakultät für Sozialwissenschaften > Sozialpsychologie und Mikrosoziologie (Stavrova 2025-)
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Bereits vorhandene Lizenz:
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 Creative Commons Namensnennung 4.0 International (CC BY 4.0)
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Fachgebiet:
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150 Psychologie
300 Sozialwissenschaften, Soziologie, Anthropologie
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Freie Schlagwörter (Englisch):
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taxation , topic modeling , machine learning , social media , information diffusion , sentiment analysis
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Abstract:
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Recent literature on taxation suggests that a “service and client” approach by the authorities is required in order to establish a synergistic tax climate between taxpayers and tax offices and thus enhance voluntary tax compliance. The present study investigates whether lay people's conceptions about taxation reflect such a synergistic (vs. an antagonistic) climate. Applying an unsupervised machine learning approach (i.e., topic modeling) to over a million tax related tweets from 2010 to 2020, we identified 30 topics with different content. Using the theoretical framework differentiating between synergistic and antagonistic tax climate, we were able to further categorize these topics into four broader groups: 1. Opinions about Tax Politics, 2. Enforcement (antagonistic climate), 3. Information & Service (synergistic climate), and 4. Emotions. The most frequently observed group was Information & Service (synergistic climate), which also steadily gained prominence during the past decade. We proceeded by analyzing the information diffusion properties and sentiment of the tweets associated with the four groups. Information & Service tweets had the most positive sentiment but were shared the least, while tweets regarding Opinions about Tax Politics were shared most often. In sum, the results suggest that lay people's conceptions about taxation – as discerned from conversations on social media (Twitter) – largely reflect a synergistic (vs. an antagonistic) climate, and contribute to the literature on tax climate and social media.
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 | Das Dokument wird vom Publikationsserver der Universitätsbibliothek Mannheim bereitgestellt. |
 | Dieser Datensatz wurde nicht während einer Tätigkeit an der Universität Mannheim veröffentlicht, dies ist eine Externe Publikation. |
 Suche Autoren in
BASE:
Puklavec, Žiga
;
Kogler, Christoph
;
Stavrova, Olga
;
Zeelenberg, Marcel
Google Scholar:
Puklavec, Žiga
;
Kogler, Christoph
;
Stavrova, Olga
;
Zeelenberg, Marcel
ORCID:
Puklavec, Žiga ; Kogler, Christoph ; Stavrova, Olga  ORCID: 0000-0002-6079-4151 ; Zeelenberg, Marcel
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