Impact of Artificial Intelligence–Generated Content Labels On Perceived Accuracy, Message Credibility, and Sharing Intentions for Misinformation: Web-Based, Randomized, Controlled Experiment
Bibliographic record
Abstract
BACKGROUND: The proliferation of generative artificial intelligence (AI), such as ChatGPT, has added complexity and richness to the virtual environment by increasing the presence of AI-generated content (AIGC). Although social media platforms such as TikTok have begun labeling AIGC to facilitate the ability for users to distinguish it from human-generated content, little research has been performed to examine the effect of these AIGC labels. OBJECTIVE: This study investigated the impact of AIGC labels on perceived accuracy, message credibility, and sharing intention for misinformation through a web-based experimental design, aiming to refine the strategic application of AIGC labels. METHODS: The study conducted a 2×2×2 mixed experimental design, using the AIGC labels (presence vs absence) as the between-subjects factor and information type (accurate vs inaccurate) and content category (for-profit vs not-for-profit) as within-subjects factors. Participants, recruited via the Credamo platform, were randomly assigned to either an experimental group (with labels) or a control group (without labels). Each participant evaluated 4 sets of content, providing feedback on perceived accuracy, message credibility, and sharing intention for misinformation. Statistical analyses were performed using SPSS version 29 and included repeated-measures ANOVA and simple effects analysis, with significance set at P<.05. RESULTS: As of April 2024, this study recruited a total of 957 participants, and after screening, 400 participants each were allocated to the experimental and control groups. The main effects of AIGC labels were not significant for perceived accuracy, message credibility, or sharing intention. However, the main effects of information type were significant for all 3 dependent variables (P<.001), as were the effects of content category (P<.001). There were significant differences in interaction effects among the 3 variables. For perceived accuracy, the interaction between information type and content category was significant (P=.005). For message credibility, the interaction between information type and content category was significant (P<.001). Regarding sharing intention, both the interaction between information type and content category (P<.001) and the interaction between information type and AIGC labels (P=.008) were significant. CONCLUSIONS: This study found that AIGC labels minimally affect perceived accuracy, message credibility, or sharing intention but help distinguish AIGC from human-generated content. The labels do not negatively impact users' perceptions of platform content, indicating their potential for fact-checking and governance. However, AIGC labeling applications should vary by information type; they can slightly enhance sharing intention and perceived accuracy for misinformation. This highlights the need for more nuanced strategies for AIGC labels, necessitating further research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".