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Electronic News Dataset for Native Advertisement Detection.

Brian Rizqi Paradisiaca Darnoto1,2, Daniel Siahaan3, Diana Purwitasari1

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This study introduces a new dataset to improve the detection of native advertising (NA), a marketing strategy that blends sponsored content with editorial material. The dataset aids in developing algorithms for greater online advertising transparency.

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Area of Science:

  • Digital Marketing
  • Computational Linguistics
  • Information Science

Background:

  • Native advertising is a prevalent online marketing strategy that integrates sponsored content seamlessly with editorial material, often deceiving readers.
  • The sophisticated integration of native ads across various formats (text, video, social media) poses significant detection challenges.
  • Ensuring transparency in online environments requires robust methodologies and comprehensive datasets for identifying native advertising.

Purpose of the Study:

  • To develop a specialized, systematically collected, and annotated dataset for enhancing native advertising detection.
  • To facilitate the creation and evaluation of advanced algorithms for identifying native ads and their implicit characteristics.
  • To promote transparency in online advertising by distinguishing sponsored content from genuine editorial material.

Main Methods:

  • Systematic data collection and annotation of native advertising content.
  • Aggregation of news articles from six major Indonesian electronic news portals.
  • Inclusion of four implicit characteristics beyond simple native ad identification.

Main Results:

  • Creation of a meticulously annotated dataset specifically designed for native ad detection.
  • The dataset serves as a crucial resource for developing and testing sophisticated detection algorithms.
  • The research provides a foundation for improved accuracy in distinguishing native ads from editorial content.

Conclusions:

  • The developed dataset significantly enhances the capability to detect native advertising.
  • This resource is vital for advancing research in automated native ad identification.
  • The findings contribute to greater transparency and ethical practices in online advertising ecosystems.