AI used for real-time selection of actionable messages for government and public health campaigns

(Press-News.org) Public health promotion campaigns can be effective, but they do not tend to be efficient. Most are time-consuming, expensive, and reliant on the intuition of creative workers who design messages without a clear sense of what will spark behavioral change. A new study conducted by Dolores Albarracín and Man-pui Sally Chan of the University of Pennsylvania, government and community agencies, and researchers at the University of Illinois and Emory University suggests that artificial intelligence (AI) can facilitate theory- and evidence-based message selection.

The research group, led by Albarracín, a social psychologist who is the Amy Gutmann Penn Integrates Knowledge University Professor and director of the Annenberg Public Policy Center’s Communication Science Division, developed a series of computational processes to automatically generate an HIV prevention and testing campaign for counties in the United States, using real-time social media as a source for messages. The paper, whose lead author is Chan, a research associate professor at Penn’s Annenberg School for Communication, describes how the method provides a living repository of messages that can be selected based on the team’s theory and AI-generated data about messages that people and institutions circulate on social media.

Social media provide a living repository of messages generated by a community, from which effective messages can be drawn and amplified. The researchers designed AI tools to gather HIV prevention and testing messages from U.S. social media posts, then curate them for “actionability” – a crucial characteristic for messages aimed at motivating action – and select posts appropriate for a targeted priority population, in this case men who have sex with men (MSM).

The researchers then conducted three studies. The first, a computational analysis, established that the AI tool successfully chose messages with the desired qualities. The second, an online experiment with men who have sex with men, showed that the resulting messages are perceived as more actionable, personally relevant, and effective by the target audience than control messages not selected by the AI tool. The third, a field experiment involving public health agencies and community-based organizations with jurisdiction in 42 counties in the United States, showed that utilizing the AI message selection process made public health agencies substantially more likely to post HIV prevention messages on social media.

As part of the study, the researchers also tested messages that were vetted by a human researcher after being selected by the AI process against messages that were not vetted. AI-selected messages outperformed control messages in reported effectiveness, regardless of whether they were vetted, but vetted messages performed better than unvetted ones. Regardless of the advantage of vetting in terms of efficacy, the researchers caution that a brief human vetting process must be included as part of this method to avoid harmful content and misinformation.

This study, published recently in PNAS Nexus offers the first empirical evidence for the successful automatic selection of public health messages for community and government dissemination. Chan says this is a promising development. “AI processes like this one can provide an inexpensive and creative way for public health agencies to disseminate effective messages.” Albarracín concurs that “The era of AI will accelerate our ability to use theory and empirical evidence in rapid and continuous campaigns generation.”

“Living health-promotion campaigns for communities in the United States: Decentralized content extraction and sharing through AI,” was published in June 2025 in PNAS Nexus. See the paper for a full list of authors and affiliations. DOI: 10.1093/pnasnexus/pgaf171. 

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