Knowledge behavior model of e-government social media users

Daphna Shwartz-Asher, Soon Ae Chun, Nabil R. Adam

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Purpose: A social media user behavior model is presented as a function of different user types, i.e. light and heavy users. The users’ behaviors are analyzed in terms of knowledge creation, framing and targeting. Design/methodological approach: Data consisting of 160,000 tweets by nearly 40,000 twitter users in the city of Newark (NJ, USA) were collected during the year 2014. An analysis was conducted to examine the hypothesis that different user types exhibit distinct behaviors driven from different motivations. Findings: There are three important findings of this study. First, light users reuse existing content more often, while heavy and automated users create original content more often. Light users also use more sentiments than the heavy and automated users. Second, automated users frame more than heavy users, who frame more than light users. Third, light users tend to target a specific audience, while heavy and automated users broadcast to a general audience. Research implications: Decision-makers can use this study to improve communication with their customers (the public) and allocate resources more effectively for better public services. For example, they can better identify subsets of users and then share and track specialized content to these subsets more effectively. Originality/value: Despite the broad interest, there is insufficient research on many aspects of social media use, and very limited empirical research examining the relevance and impact of social media within the public sector. The social media user behavior model was established as a framework that can provide explanations for different social media knowledge behaviors exhibited by various subsets of users, in an e-government context.

Original languageEnglish (US)
Pages (from-to)456-475
Number of pages20
JournalTransforming Government: People, Process and Policy
Issue number3
StatePublished - 2017

All Science Journal Classification (ASJC) codes

  • Public Administration
  • Computer Science Applications
  • Information Systems and Management


  • Knowledge creation
  • Knowledge framing
  • Knowledge targeting
  • Social media


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