Challenge: a new study examines the media coverage of police violence in the United States by examining the framing of 82k news articles spanning 7k police killings.
Approach: They propose an NLP framework to measure entity-centric framing to understand media coverage on police violence in the United States in a new police violence frames corpus of 82k news articles spanning 7k police killings.
Outcome: The proposed framework reveals significant differences in the way liberal and conservative news sources frame both the issue of police violence and the entities involved.

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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 .
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.
Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings (N19-1)

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Challenge: a new framework for studying political polarization in social media is needed to understand how group divisions manifest in language.
Approach: They propose to cluster tweet embeddings to uncover four dimensions of political polarization in social media . their results apply existing lexical methods to analyze 4.4M tweets on 21 mass shootings .
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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.
Not all ANIMALs are equal: metaphorical framing through source domains and semantic frames (2026.findings-acl)

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Challenge: a computational framework allows to derive discourse metaphors through their source domains and semantic frames.
Approach: They propose a computational framework that allows to derive salient discourse metaphors through their source domains and semantic frames.
Outcome: The proposed framework uncovers well-known source domains and reveals nuanced frame-level associations that distinguish how the issue is portrayed.
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.
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 .
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 .
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.
BU-NEmo: an Affective Dataset of Gun Violence News (2022.lrec-1)

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Challenge: Using a dataset that contains headline and image pairings from 840 news articles, we explore the relationship between image and text influence on human emotional response.
Approach: They propose to use a U.S. gun violence news dataset that contains headline and image pairings from 840 news articles with 15K high-quality crowdsourced annotations on emotional responses.
Outcome: The proposed dataset includes annotations on the dominant emotion experienced with the content, the intensity of the selected emotion and an open-ended, written component.

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