# Finding frames with BERT: A transformer-based approach to generic news frame detection

arXiv: https://arxiv.org/abs/2409.00272 · HTML: https://mlsanity.com/paper/2409.00272/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 31 references that are in the map.
- First posted on arXiv 30 Aug 2024.

## Builds on (most cited first, 2 of 2)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Detecting Frames in News Headlines and Lead Images in U.S. Gun Violence Coverage](https://mlsanity.com/paper/2406.17213/) (2024)

## Built on by (most cited first, 1 of 1)

- [Listening Between the Lines: Decoding Podcast Narratives with Language Modeling](https://mlsanity.com/paper/2511.05310/) (2025)

## Abstract (arXiv)

Framing is among the most extensively used concepts in the field of communication science. The availability of digital data offers new possibilities for studying how specific aspects of social reality are made more salient in online communication but also raises challenges related to the scaling of framing analysis and its adoption to new research areas (e.g. studying the impact of artificial intelligence-powered systems on representation of societally relevant issues). To address these challenges, we introduce a transformer-based approach for generic news frame detection in Anglophone online content. While doing so, we discuss the composition of the training and test datasets, the model architecture, and the validation of the approach and reflect on the possibilities and limitations of the automated detection of generic news frames.