Skip to main content
An official website of the United States government
Grant Details

Grant Number: 5R01CA160226-04 Interpret this number
Primary Investigator: Cappella, Joseph
Organization: University Of Pennsylvania
Project Title: Constructing Recommender Systems for Effective Health Messages: Smoking Cessation
Fiscal Year: 2014


DESCRIPTION (provided by applicant): Successful public health campaigns depend in large measure on how effectively information is communicated. Designing effective messages for the public's health is both an art and a science with the art dominating because knowledge generated by the science is accumulating too slowly and with insufficient theoretical guidance. The research proposed here abandons standard experimental approaches to message design and abandons theory development in favor of the development of a "recommendation machine" modeled after commercial systems. This approach will allow message recommendations tailored to individual preferences based on algorithms for content similarity, preference similarity or their combination. Recommendation systems are essentially derived algorithms operating on dense data involving both preferences for messages (ratings by smokers) and objective message features (content). Their goal is to predict a user's ratings for messages not previously seen by the user. Conventional approaches to message research advance the science of message design too slowly, are driven by inadequate theory, and require very complex factorial interactions among audience characteristics, message features and the target behavior. The development of a recommendation machine for health messages will operate on a large archive of messages, dense preference data from smokers, and extensive (and mostly automated) assessment of the objective features of messages. The results will provide a procedure for the selection of effective messages from a large archive that will be tailored to a specific target person. Unlike tailoring research, no a priori assumptions will be made about which audience characteristics would need to be identified to constrain message selection. Recommendation systems have the potential to transform research about effective messages. The outcomes would include (1) an algorithm for preferences for effective (smoking cessation) messages; (2) a leap beyond approaches to message design side-stepping the tedious work in one-feature-at-a-time experiments; (3) an approach employing methods familiar to anyone ever having bought a book on Amazon or selected a movie via Netflix; (4) setting the stage for automatic user friendly recommender systems. Message selection processes for behaviors to increase health and lower risk would change radically. Applications using new media such as mobile technologies and personalized health web sites would be enabled as well. The research proposed: (1) prepares existing data to use in pretesting recommendation systems; (2) develops recommendation algorithms that are hybrids of collaborative and content approaches using state-of- the-art procedures from the commercial arena; (3) tests hybrid algorithms in a sample of smokers comparing the preferences for recommended messages to two comparison conditions; (4) follows up to determine whether differences in smoking cessation intentions differ between those receiving messages suggested via the recommender algorithms vs. those receiving a random selection or a "most preferred" set.


How Message Features and Social Endorsements Affect the Longevity of News Sharing.
Authors: Kim H.S. .
Source: Digital journalism (Abingdon, England), 2021; 9(8), p. 1162-1183.
EPub date: 2020-09-04.
PMID: 34900400
Related Citations

An Efficient Message Evaluation Protocol: Two Empirical Analyses on Positional Effects and Optimal Sample Size.
Authors: Kim M. , Cappella J.N. .
Source: Journal of health communication, 2019; 24(10), p. 761-769.
EPub date: 2019-09-21.
PMID: 31543057
Related Citations

When Similarity Strikes Back: Conditional Persuasive Effects of Character-Audience Similarity in Anti-Smoking Campaign.
Authors: Kim M. .
Source: Human communication research, 2019 Jan; 45(1), p. 52-77.
EPub date: 2018-10-01.
PMID: 30631219
Related Citations

Vectors into the Future of Mass and Interpersonal Communication Research: Big Data, Social Media, and Computational Social Science.
Authors: Cappella J.N. .
Source: Human communication research, 2017 Oct; 43(4), p. 545-558.
EPub date: 2017-06-30.
PMID: 29104350
Related Citations

Effect of Character-Audience Similarity on the Perceived Effectiveness of Antismoking PSAs via Engagement.
Authors: Kim M. , Shi R. , Cappella J.N. .
Source: Health communication, 2016 Oct; 31(10), p. 1193-204.
EPub date: 2016-02-18.
PMID: 26891148
Related Citations

Attracting Views and Going Viral: How Message Features and News-Sharing Channels Affect Health News Diffusion.
Authors: Kim H.S. .
Source: The Journal of communication, 2015-06-01; 65(3), p. 512-534.
EPub date: 2015-05-14.
PMID: 26441472
Related Citations

Content characteristics driving the diffusion of antismoking messages: implications for cancer prevention in the emerging public communication environment.
Authors: Kim H.S. , Lee S. , Cappella J.N. , Vera L. , Emery S. .
Source: Journal of the National Cancer Institute. Monographs, 2013 Dec; 2013(47), p. 182-7.
PMID: 24395989
Related Citations

Back to Top