The Automated Copywriter: Algorithmic Rephrasing of Health-Related Advertisements to Improve their Performance

Brit Youngmann, Elad Yom-Tov, Ran Gilad-Bachrach, Danny Karmon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Search advertising is one of the most commonly-used methods of advertising. Past work has shown that search advertising can be employed to improve health by eliciting positive behavioral change. However, writing effective advertisements requires expertise and (possible expensive) experimentation, both of which may not be available to public health authorities wishing to elicit such behavioral changes, especially when dealing with a public health crises such as epidemic outbreaks. Here we develop an algorithm which builds on past advertising data to train a sequence-to-sequence Deep Neural Network which "translates" advertisements into optimized ads that are more likely to be clicked. The network is trained using more than 114 thousands ads shown on Microsoft Advertising. We apply this translator to two health related domains: Medical Symptoms (MS) and Preventative Healthcare (PH) and measure the improvements in click-through rates (CTR). Our experiments show that the generated ads are predicted to have higher CTR in 81% of MS ads and 76% of PH ads. To understand the differences between the generated ads and the original ones we develop estimators for the affective attributes of the ads. We show that the generated ads contain more calls-to-action and that they reflect higher valence (36% increase) and higher arousal (87%) on a sample of 1000 ads. Finally, we run an advertising campaign where 10 random ads and their rephrased versions from each of the domains are run in parallel. We show an average improvement in CTR of 68% for the generated ads compared to the original ads. Our results demonstrate the ability to automatically optimize advertisement for the health domain. We believe that our work offers health authorities an improved ability to help nudge people towards healthier behaviors while saving the time and cost needed to optimize advertising campaigns.

Original languageEnglish
Title of host publicationThe Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020
PublisherAssociation for Computing Machinery, Inc
Pages1366-1377
Number of pages12
ISBN (Electronic)9781450370233
DOIs
StatePublished - 20 Apr 2020
Externally publishedYes
Event29th International World Wide Web Conference, WWW 2020 - Taipei, Taiwan, Province of China
Duration: 20 Apr 202024 Apr 2020

Publication series

NameThe Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020

Conference

Conference29th International World Wide Web Conference, WWW 2020
Country/TerritoryTaiwan, Province of China
CityTaipei
Period20/04/2024/04/20

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