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.

Similar Papers

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.
CLoSE: Contrastive Learning of Subframe Embeddings for Political Bias Classification of News Media (2022.coling-1)

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Challenge: Framing is a political strategy in which journalists and politicians emphasize certain aspects of an issue to influence and sway public opinion.
Approach: They propose a BERT-based model which embeds indicators of frames from news articles in order to predict political bias.
Outcome: The proposed model performs on subframes and political bias classification tasks and is able to detect political bias on both zero-shot and few-shot learning tasks.
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 .
Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media (2020.emnlp-main)

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Challenge: a new study suggests a minimally supervised approach for identifying nuanced political frames in news articles on politically divisive topics.
Approach: They propose a minimally supervised approach for identifying nuanced policy frames in news coverage of politically divisive topics.
Outcome: The proposed subframes can capture differences in political ideology better . the proposed frameworks were tested on immigration, gun control and abortion topics .
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 .
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.
Controlled Neural Sentence-Level Reframing of News Articles (2021.findings-emnlp)

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Challenge: a news article is framed from a specific perspective, but reframing can be difficult . a framed article can be used to communicate with opposing camps of audiences .
Approach: They propose to reframe news articles using a media frame corpus to achieve this . they propose three strategies to train neural models for reframing .
Outcome: The proposed techniques maintain coherence of sentences and reframe them correctly . the proposed techniques are effective but have tradeoffs .
Entity Framing and Role Portrayal in the News (2025.findings-acl)

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Challenge: a dataset of news articles containing 22 fine-grained characters is annotated for entity framing and role portrayal . the dataset includes 1,378 recent news articles in five languages focusing on the Ukraine-Russia War and climate change .
Approach: They propose a multilingual and hierarchical corpus annotated for entity framing and role portrayal in news articles.
Outcome: The proposed dataset includes 1,378 recent news articles in five languages focusing on the Ukraine-Russia War and climate change . the authors report evaluation results on state-of-the-art multilingual transformers and hierarchical zero-shot learning using LLMs at the level of a document, paragraph, and sentence .
Conflicts, Villains, Resolutions: Towards models of Narrative Media Framing (2023.acl-long)

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Challenge: a growing body of work attempts to automatically detect media frames in the news or social media, but most adopts a topic-like view on frames, evading modelling the broader document-level narrative.
Approach: They propose an annotation paradigm that breaks a complex annotation task into a series of simple binary questions.
Outcome: The proposed method is both effective and transparent in its predictions.
Issue Framing in Online Discussion Fora (N19-1)

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Challenge: In online discussion fora, speakers often make arguments by highlighting certain aspects of the topic.
Approach: They propose to use a newswire and social media annotated corpus to detect issue frames in online discussions.
Outcome: The proposed model can be applied to the domain of discussion fora using multi-task and adversarial training.

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