Cited by 31Public Relations and Crisis CommunicationDigital Marketing and Social MediaCorporate Identity and Reputation

Inauthentic communication, organization-public relationships, and trust: A content analysis of online astroturfing news coverage

Sisson, Diana C.·2017·Public Relations Review

Integrity signals

Registry

Crossref · 10.1016/j.pubrev.2017.05.003

Cites

42 of 42 references verified0 citation errors · 0 not checkable

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Claims the paper makes

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Claims other papers make about this one

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  • Astroturfing, as a form of insincere communication, significantly undermines authenticity and trust in online relationships between organizations and the public.

    “astroturfing, as a form of insincere communication, significantly undermines authenticity and trust in online relationships between organizations and the public”

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5 of the 47 references couldn’t be matched to a known paper.

Cited by

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Authors
Sisson, D. C.
Affiliations
Auburn University (US)
Corresponding author
Sisson, Diana C.
Venue
Public Relations Review
Publisher
Elsevier BV
Published
2017-11
Volume
43
Issue
4
Pages
788-795
ISSN
0363-8111 (ISSN-L)
OpenAlex
W2622282773
Cited by
30
References
47
Citations by year
2026: 1 · 2025: 1 · 2024: 1 · 2023: 6 · 2022: 1 · 2021: 6 · 2020: 4 · 2019: 5 · 2018: 4 · 2017: 1
FWCI
8.91
Type
journal article
Language
en
Open access
closed
Locations
Public Relations Review (publishedVersion)
Topics
  • Social Sciences › Communication › Public Relations and Crisis Communication
  • Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
  • Social Sciences › Business, Management and Accounting › Strategy and Management › Corporate Identity and Reputation
Keywords
astroturfing, organizational trust, organization-public relationships, relationship management, authenticity, media coverage, online communication, networking strategies

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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