Analyzing Framing through the Casts of Characters in the News

Card, Dallas; Gross, Justin H.; Boydstun, Amber E.; Smith, Noah A.

doi:10.18653/v1/d16-1148

Cited by 55 publications

(48 citation statements)

References 48 publications

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“…To select hyperparameters, we used a small set of examples from the corpus as a development set. Then, we report average accuracy across 10-fold cross validation as in (Card et al, 2016).…”

Section: Datasetsmentioning

confidence: 99%

Neural Discourse Structure for Text Categorization

Ji¹,

Smith²

2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 1: Long Papers)

Self Cite

122

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We show that discourse structure, as defined by Rhetorical Structure Theory and provided by an existing discourse parser, benefits text categorization. Our approach uses a recursive neural network and a newly proposed attention mechanism to compute a representation of the text that focuses on salient content, from the perspective of both RST and the task. Experiments consider variants of the approach and illustrate its strengths and weaknesses.

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“…To select hyperparameters, we used a small set of examples from the corpus as a development set. Then, we report average accuracy across 10-fold cross validation as in (Card et al, 2016).…”

Section: Datasetsmentioning

confidence: 99%

Neural Discourse Structure for Text Categorization

Ji¹,

Smith²

2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 1: Long Papers)

Self Cite

122

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show abstract

“…In this work, we show how to leverage these embeddings to conduct entity-centric analyses, which broadly seek to address how entities are portrayed in narrative text (Bamman et al, 2013;Card et al, 2016). For instance, in the sentence "Batman apprehends the Joker", a reader might infer that Batman is good, the Joker is evil, and Batman is more powerful than the Joker.…”

Section: Introductionmentioning

confidence: 99%

Entity-Centric Contextual Affective Analysis

Field

Tsvetkov

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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While contextualized word representations have improved state-of-the-art benchmarks in many NLP tasks, their potential usefulness for social-oriented tasks remains largely unexplored. We show how contextualized word embeddings can be used to capture affect dimensions in portrayals of people. We evaluate our methodology quantitatively, on held-out affect lexicons, and qualitatively, through case examples. We find that contextualized word representations do encode meaningful affect information, but they are heavily biased towards their training data, which limits their usefulness to in-domain analyses. We ultimately use our method to examine differences in portrayals of men and women.

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“…Primary frame identification is a 15-class classification problem. Two prior studies evaluate models on this task: Card et al (2016) and Ji and Smith (2017). Following these studies, we evaluate our model using 10-fold cross-validation on only the "Immigration" subset of the MFC.…”

Section: English Evaluationsmentioning

confidence: 99%

Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies

Field¹,

Kliger²,

Wintner³

et al. 2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

101

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Amidst growing concern over media manipulation, NLP attention has focused on overt strategies like censorship and "fake news". Here, we draw on two concepts from the political science literature to explore subtler strategies for government media manipulation: agenda-setting (selecting what topics to cover) and framing (deciding how topics are covered). We analyze 13 years (100K articles) of the Russian newspaper Izvestia and identify a strategy of distraction: articles mention the U.S. more frequently in the month directly following an economic downturn in Russia. We introduce embedding-based methods for cross-lingually projecting English frames to Russian, and discover that these articles emphasize U.S. moral failings and threats to the U.S. Our work offers new ways to identify subtle media manipulation strategies at the intersection of agenda-setting and framing.

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Analyzing Framing through the Casts of Characters in the News

Cited by 55 publications

References 48 publications

Neural Discourse Structure for Text Categorization

Neural Discourse Structure for Text Categorization

Entity-Centric Contextual Affective Analysis

Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies

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