Comparing the willingness to share for human-generated vs. AI-generated fake news

TL;DR

AI-generated fake news is perceived as less accurate than human-generated fake news, but both tend to be shared equally, and several socio-economic factors explain who falls for AI-generated fake news.

Abstract

Generative artificial intelligence (AI) presents large risks for society when it is used to create fake news. A crucial factor for fake news to go viral on social media is that users share such content. Here, we aim to shed light on the sharing behavior of users across human-generated vs. AI-generated fake news. Specifically, we study: (1) What is the perceived veracity of human-generated fake news vs. AI-generated fake news? (2) What is the user's willingness to share human-generated fake news vs. AI-generated fake news on social media? (3) What socio-economic characteristics let users fall for AI-generated fake news? To this end, we conducted a pre-registered, online experiment with $N=$ 988 subjects and 20 fake news from the COVID-19 pandemic generated by GPT-4 vs. humans. Our findings show that AI-generated fake news is perceived as less accurate than human-generated fake news, but both tend to be shared equally. Further, several socio-economic factors explain who falls for AI-generated fake news.

Figures (7)

• Figure 1: The experiment's procedure flowchart.
• Figure 2: Distribution of perceived veracity scores across human-generated vs. AI-generated fake news.
• Figure 3: The average rate of incorrect perceived veracity assessments across subjects. Welch's $t$-test shows that human-generated fake news has a significantly ($p < 0.001$) higher rate of incorrect perceived veracity assessments compared to AI-generated fake news.
• Figure 4: Subjects' fake news willingness to share grouped by source of generation. The $\chi^2$-test shows that there is no significant difference between AI-generated vs. human-generated fake news.
• Figure 5: Estimates explaining the perceived veracity through various socio-economic variables using linear mixed-effects regression models. All models include subject-level random effects.