Multi-Modal Framing Analysis of News (2025.emnlp-main)

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Challenge: Automated frame analysis of political communication has been limited by the use of predefined frames and the visual contexts in which they appear.
Approach: They propose a method for doing multi-modal, multi-label framing analysis at scale using large (vision-) language models.
Outcome: The proposed method provides a more complete picture for understanding media bias.

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Narratives at Conflict: Computational Analysis of News Framing in Multilingual Disinformation Campaigns (2024.acl-srw)

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Challenge: Existing methods for multilingual framing differ from those used in English-speaking world . framers often use loaded vocabularies to create political images or favor a particular point of view .
Approach: They use eight years of Russian-backed disinformation campaigns to examine framing . they find that disinformation campaign consistently favors specific framers .
Outcome: The proposed method underperforms and shows high disagreements in Russian-language articles . the proposed method is based on eight years of Russian-backed disinformation campaigns .
Multi-Label and Multilingual News Framing Analysis (2020.acl-main)

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Challenge: Recent studies have focused on news framing in English, but few studies have explored how it can be extended to other languages and in multi-label settings.
Approach: They propose a method that leverages dictionary and few annotations to detect frames from just the headline in a low-resource context.
Outcome: The proposed method performs better than translating the entire headline to the source language . it can be scaled up to many languages, even those without existing translation technologies .
A Study on Scaling Up Multilingual News Framing Analysis (2024.findings-naacl)

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Challenge: Existing studies on media framing have focused on English only data, leaving a gap in research concerning multilingual contexts.
Approach: They propose to use crowd-sourced datasets to automate framing analysis by automating translation and annotation.
Outcome: The proposed system improves on existing models in Bengali and Portuguese . the proposed system can train on a crowd-sourced dataset in 12 languages .
Detecting Frames in News Headlines and Lead Images in U.S. Gun Violence Coverage (2021.findings-emnlp)

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Challenge: Journalists have been using both text and images to frame news stories . lead images may carry additional background knowledge about the event .
Approach: They find that combining lead images and contextual information with text improves news framing . they release the first multimodal news framming dataset related to gun violence in the u.s.
Outcome: The study shows that combining lead images with text improves prediction of news frames . it also shows that using multiple modes of information improves frame image relevance .
Framing Unpacked: A Semi-Supervised Interpretable Multi-View Model of Media Frames (2021.naacl-main)

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Challenge: Existing models for news analysis lack transparency in their predictions.
Approach: They propose a semi-supervised model that embeds local information into news articles . it can be used to improve automatic news analysis, authors argue .
Outcome: The proposed model outperforms previous models and can be used with unlabeled training data.
OpenFraming: Open-sourced Tool for Computational Framing Analysis of Multilingual Data (2021.emnlp-demo)

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Challenge: Existing frameworks for analyzing frames in multilingual text documents are available online and via an API.
Approach: They propose a web-based system for analyzing frames in multilingual text documents . framework combines unsupervised and supervised machine learning and leverages a state-of-the-art multilingual language model .
Outcome: The proposed framework can significantly improve frame prediction performance while requiring a small sample of manual annotations.
A Survey of Computational Framing Analysis Approaches (2022.emnlp-main)

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Challenge: Existing computational methods for framing analysis are limited . a lack of a comprehensive understanding of framability is limiting the research .
Approach: They propose to combine existing approaches to analyze large-scale datasets using computational methods.
Outcome: The proposed methods will help scholars better understand how frames are being explored computationally, the authors argue .
Media Attitude Detection via Framing Analysis with Events and their Relations (2024.emnlp-main)

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Challenge: a recent study examined the effects of media framing on public perception and understanding of news articles.
Approach: They propose to extract framing devices employed by media to assess their role in framating the narrative.
Outcome: The proposed method surpasses baseline models and offers a more detailed and explainable analysis of media framing effects.
Media Bias, the Social Sciences, and NLP: Automating Frame Analyses to Identify Bias by Word Choice and Labeling (2020.acl-srw)

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Challenge: slanted news coverage can have negative effects on individuals and society . a system that helps readers to become aware of the differences in media coverage caused by bias is being developed.
Approach: They propose to use natural language processing and deep learning to identify instances of WCL bias and estimate the frames they induce.
Outcome: The proposed system can identify instances of WCL bias and estimate the frames they induce.
Modeling Framing in Immigration Discourse on Social Media (2021.naacl-main)

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Challenge: Using a dataset of immigration-related tweets, we examine how ordinary people on social media frame political issues.
Approach: They propose to use a dataset of immigration-related tweets labeled for multiple framing typologies from political communication theory to analyze framers.
Outcome: The proposed model enables comparisons between different types of frames on social media and a dataset of immigration-related tweets.

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