Challenge: Edited media frames are structured annotations with respect to intents, emotional reactions, attacks on individuals, and the implications of disinformation.
Approach: They propose a new formalism to understand visual media manipulation as structured annotations with respect to intents, emotional reactions, attacks on individuals, and the implications of disinformation.
Outcome: The proposed model obtains promising results on a dataset with 56k question-answer pairs written in rich natural language.

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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 .
Misinfo Reaction Frames: Reasoning about Readers’ Reactions to News Headlines (2022.acl-long)

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Challenge: Empirical results confirm that it is indeed possible for neural models to predict the prominent patterns of readers’ reactions to previously unseen news headlines.
Approach: They propose a pragmatic formalism for modeling how readers might react to a news headline . they propose 'misinfo' frames, which can be used to model reader perceptions of news reliability .
Outcome: The proposed model can predict readers' reactions to previously unseen headlines.
MIPD: Exploring Manipulation and Intention In a Novel Corpus of Polish Disinformation (2024.emnlp-main)

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Challenge: Using a unique methodology, we annotated disinformation in Polish with multiple labels indicating both intents and manipulation techniques employed.
Approach: They present a novel corpus of 15,356 Polish web articles annotated with multiple labels indicating both disinformation creators’ intents and manipulation techniques employed.
Outcome: The proposed dataset sheds light on the authors' intention and manipulation techniques in disinformation.
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.
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.
An Interactive Framework for Profiling News Media Sources (2024.naacl-long)

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Challenge: Existing tools for detecting fake news are difficult for automated systems . e.g., we focus on the source level, and ask: Is this source factual or politically biased?
Approach: They propose an interactive framework for news media profiling that uses graphs and pre-trained large language models to characterize social context on social media.
Outcome: The proposed framework can detect fake and biased news media with as little as 5 human interactions . it can scale better, as often sources publish have same factuality/political bias as source .
Edit me: A Corpus and a Framework for Understanding Natural Language Image Editing (L18-1)

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Challenge: a corpus of image edit requests is elicited for real world images, and an annotation framework is developed . evaluators evaluate crowd-sourced annotation as a means of efficiently creating a sizable corpus at a reasonable cost.
Approach: They propose a natural language interface for interacting with an image editing program . they propose an annotation framework for understanding natural language requests .
Outcome: The proposed tool interprets image edit requests and maps them to actionable commands.
A Structured Clustering Approach for Inducing Media Narratives (2026.acl-long)

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Challenge: Existing approaches to modeling media narratives miss subtle narrative patterns through coarse-grained analysis or require domain-specific taxonomies that limit scalability.
Approach: They propose a framework for inducing rich narrative schemas by jointly modeling events and characters via structured clustering.
Outcome: The proposed framework produces explainable narrative schemas that align with established framing theory while scaling to large corpora without exhaustive manual annotation.
Modeling Frames in Argumentation (D19-1)

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Challenge: In argumentation, framing is used to emphasize a specific aspect of a topic while concealing others.
Approach: They propose an unsupervised method for framing arguments into non-overlapping frames . authors propose a corpus of 12, 326 debate-portal arguments organized along the frames of debates' topics .
Outcome: The proposed method outperforms baselines on the argumentation task by 0.28 points.
Social Story Frames: Contextual Reasoning about Narrative Intent and Reception (2026.acl-long)

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Challenge: SocialStoryFrames is a formalism for distilling plausible inferences about reader response . authors characterize frequency and interdependence of storytelling intents across communities .
Approach: They propose a formalism for distilling plausible inferences about reader response using conversational context and a taxonomy grounded in narrative theory, linguistic pragmatics, and psychology.
Outcome: The proposed model can be used to analyze reader responses in online communities.

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