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

3 Citations (Scopus)


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
Number of pages22
JournalDigital Journalism
Publication statusAccepted/In press - 15 Feb 2022
Externally publishedYes


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

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