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What we tweet about when we tweet about taxes: A topic modelling approach

  • Žiga Puklavec*
  • , Christoph Kogler
  • , Olga Stavrova
  • , Marcel Zeelenberg
  • *Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)1242-1254
Number of pages13
JournalJournal of Economic Behavior and Organization
Volume212
DOIs
Publication statusPublished - Aug 2023

Bibliographical note

Publisher Copyright:
© 2023 The Author(s)

Keywords

  • Information diffusion
  • Machine learning
  • Sentiment analysis
  • Social media
  • Taxation
  • Topic modeling

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