Proposing an open-sourced tool for computational framing analysis of multilingual data

Lei Guo, Chao Su, Sejin Paik, Vibhu Bhatia, Vidya Prasad Akavoor, Ge Gao, Margrit Betke, Derry Wijaya

Research output: Contribution to journalArticleResearchpeer-review

12 Citations (Scopus)

Abstract

We propose a five-step computational framing analysis framework that researchers can use to analyze multilingual news data. The framework combines unsupervised and supervised machine learning and leverages a state-of-the-art multilingual deep learning model, which can significantly enhance frame prediction performance while requiring a considerably small sample of manual annotations. Most importantly, anyone can perform the proposed computational framing analysis using a free, open-sourced system, created by a team of communication scholars, computer scientists, web designers and web developers. Making advanced computational analysis available to researchers without a programming background to some degree bridges the digital divide within the communication research discipline in particular and the academic community in general.

Original languageEnglish
Pages (from-to)276-297
Number of pages22
JournalDigital Journalism
Volume11
Issue number2
DOIs
Publication statusPublished - 2023
Externally publishedYes

Keywords

  • BERT
  • deep learning
  • Framing
  • machine learning
  • topic modeling

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