Cue to Trust? Investigating the Impact of Political Advertising Transparency Disclaimers on Citizen Trust Evaluations
Abstract
Citizens in a democracy must navigate an increasingly dense information landscape. Regulation can aid this navigation by mandating disclosures of the source and nature of political campaign material. In many countries, legislators are increasing transparency requirements for online advertising in particular. The current paper looks at how and if citizens use such disclaimers to infer the intent of political advertisers during the process of a trust evaluation. This paper describes a survey experiment that specifically investigates evaluations of unknown campaigners, theorising such conditions will maximise any effect disclaimers have on trust. Testing both sponsorship and micro-targeting disclaimers, no support is found for the theoretical claim that viewing a disclaimer can increase how trustworthy a political advertiser is perceived to be. There is preliminary support that, for some individuals, viewing a disclaimer increases scepticism.
Study specs
- Authors
- HC Gordon,T Stafford,K Dommett
- Discipline
- Political Science,Communication
- Year
- 2024
- Human Data Platform
- Prolific
- Source
- View Source Google Scholar
Peer Review & Critical Discussion
Potential Selection Bias in 2023 Cohort
The participant pool shows a concerning overrepresentation of users from high-income demographics. Looking at Table 3, we can see that 78% of respondents had annual incomes above $75k, which significantly limits the generalizability of these findings to broader populations.
Non-naive Participants Issue
I've noticed a methodological concern regarding participant naivety. Given that Prolific users often complete multiple studies, there's a real risk that participants had prior exposure to similar experimental paradigms, which could confound the results.
RLHF Applicability to This Study Design
The implications for RLHF training pipelines are understated. If we accept the authors' conclusions about preference stability, this has direct consequences for how we should structure reward model training. The temporal decay effect described in Section 4.2 is particularly relevant.
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