Tokenization of social media engagements increases the sharing of false (and other) news but penalization moderates it
Abstract
Some major social media companies are announcing plans to tokenize user engagements, derived from blockchain-based decentralized social media. This would bring financial and reputational incentives for engagement, which might lead users to post more objectionable content. Previous research showed that financial or reputational incentives for accuracy decrease the willingness to share misinformation. However, it is unclear to what extent such outcome would change if engagements instead of accuracy were incentivized, which is a more realistic scenario. To address this question, we conducted a survey experiment to examine the effects of hypothetical token incentives. We find that a simple nudge about the possibility of earning token-based points for the achieved user engagements increases the willingness to share different kinds of news, including misinformation. The presence of penalties for objectionable posts diminishes the positive effect of tokenization rewards on misinformation sharing, but it does not eliminate it. These results have policy implications for content moderation practices if platforms embrace decentralization and engagement tokenization.
Study specs
Survey experiment analyzing the impact of hypothetical token rewards and penalties on user willingness to share different types of news content.
- Authors
- M Alizadeh,E Hoes,F Gilardi
- Institution
- Department of Marketing,University of Amsterdam,Department of Social Sciences,Università Degli Studi di Milano,Department of Political Science and International Relations,Università Degli Studi di Milano
- Discipline
- Computational Social Science
- Study Type
- Survey Research
- Year
- 2025
- Human Data Platform
- Prolific
- Source
- View Source DOI Google Scholar
Measured Outcomes
Effect of token-based incentives and penalties on user engagement and the willingness to share misinformation.
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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