Abstract

Despite Meta’s efforts to promote health information in the COVID-19 pandemic, the growing number of ads is making online content control extremely challenging. To effectively categorize the ads, this work investigates the major discourses shared across Meta ads with various categories related to COVID-19. We propose an interpretable classification model that captures common discourses in the form of keywords and phrases in ads. Particularly, we propose to use hypergraph to connect ads and discourses to capture their high-order interactions. Experiments on a curated Meta Ads dataset show that our model can provide subject-specific discourses and improve classification performance significantly.

Original languageEnglish (US)
Title of host publicationSocial, Cultural, and Behavioral Modeling - 15th International Conference, SBP-BRiMS 2022, Proceedings
EditorsRobert Thomson, Christopher Dancy, Aryn Pyke
PublisherSpringer Science and Business Media Deutschland GmbH
Pages35-45
Number of pages11
ISBN (Print)9783031171130
DOIs
StatePublished - 2022
Event15th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation Conference, SBP-BRiMS 2022 - Pittsburgh, United States
Duration: Sep 20 2022Sep 23 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13558 LNCS

Conference

Conference15th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation Conference, SBP-BRiMS 2022
Country/TerritoryUnited States
CityPittsburgh
Period9/20/229/23/22

Keywords

  • Hypergraph
  • Meta advertisement
  • Text classification

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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