Impact of Artificial Intelligence–Generated Content Labels On Perceived Accuracy, Message Credibility, and Sharing Intentions for Misinformation: Web-Based, Randomized, Controlled Experiment

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.