Challenge: State-run newspapers are believed to strategically select and frame news articles to align with the shifting political tides of the country.
Approach: They analyze more than 50 years of articles from the People's Daily and Reference News to quantify differences in content and framing over time.
Outcome: The proposed methods show that the changes in name mentions and sentiment in news articles are more significant in People’s Daily than in Reference News .

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Uncovering Temporal Framing in the News (2026.acl-long)

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Challenge: Temporal language is used to structure meaning rather than report chronology in news discourse . a recent study focused on temporal expression extraction and temporal reasoning .
Approach: They propose a taxonomy of eight temporal frames grounded in prior work on time and framing . they analyze frame prevalence, co-occurrence patterns, and lexical cues from a news corpus .
Outcome: The proposed taxonomy outperforms zero-shot models at the sentence level . it shows that temporal framing is learnable at the sentences level compared to other methods .
Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies (D18-1)

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Challenge: Amidst growing concern over media manipulation, NLP studies focus on overt strategies like censorship and “fake news”.
Approach: They propose to use two concepts from political science literature to identify subtler media manipulation strategies . they propose to apply embedding-based methods to cross-lingually project English frames to Russian .
Outcome: The proposed techniques can be applied to 13 years of the Russian newspaper Izvestia and show that they highlight U.S. moral failings and threats to the U.s.
Hong Kong: Longitudinal and Synchronic Characterisations of Protest News between 1998 and 2020 (2022.lrec-1)

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Challenge: This paper examines the utility and timeliness of the Hong Kong Protest News Dataset . it sheds light on whether depth and/or manner of reporting changed over time .
Approach: They use the Hong Kong Protest News Dataset to investigate synchronic news characterisations of protests in Hong Kong between 1998 and 2020.
Outcome: The dataset sheds light on whether depth and/or manner of reporting changed over time, and if so, in what ways, or in response to what.
Dynamic and Static Topic Model for Analyzing Time-Series Document Collections (P18-2)

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Challenge: a collection of documents often has dynamic structures, i.e., topics evolve along time depending on multiple topics in the past.
Approach: They propose a dynamic and static topic model that considers dynamic and dynamic structures of topic evolution and static structures of the topic hierarchy at each time.
Outcome: The proposed model outperforms conventional models on scientific papers . it shows that extracted topic structures are useful for analyzing research activities .
No Permanent Friends or Enemies: Tracking Relationships between Nations from News (N19-1)

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Challenge: Understanding complex international relations is important but challenging for civilians . topic models and neural models have been proposed to explore relations without supervision .
Approach: They propose an unsupervised neural model that integrates linguistic insights into the model to infer relations between nations from news articles.
Outcome: The proposed model outperforms baselines from topic models and hidden Markov models.
Measuring and Modeling Language Change (N19-5)

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Challenge: This tutorial will help researchers answer questions fundamental to the social sciences and humanities .
Approach: This tutorial is designed to help researchers answer questions in the social sciences and humanities . it synthesizes recent computational techniques for handling and modeling temporal data .
Outcome: The tutorial will synthesize recent techniques for handling and modeling temporal data, such as dynamic word embeddings, and identify useful tools for social scientists and digital humanities scholars.
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.
Analyzing the Use of Metaphors in News Editorials for Political Framing (2024.naacl-long)

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Challenge: Existing studies on the use of metaphors in political discourses are largely unexplored.
Approach: They propose to use a dataset to study the use of metaphors in political discourses . they identify single and composite metaphors and provide annotations of their source and target domains based on the corpus .
Outcome: The proposed dataset consists of 300 news editorials with single and composite metaphors and annotations of the source and target domains for each metaphor.
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 .
The Power of Framing: How News Headlines Guide Search Behavior (2025.findings-emnlp)

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Challenge: Framing effects on judgment are well documented, but their impact on subsequent search behavior is less understood.
Approach: They conducted a controlled experiment where participants issued queries and selected headlines filtered by specific linguistic frames.
Outcome: The results suggest that even brief exposure to framing can meaningfully alter the direction of users’ information-seeking behavior.

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