Media Framing through the Lens of Event-Centric Narratives

• URL: http://arxiv.org/abs/2410.03151v1
• Date: Fri, 4 Oct 2024 05:21:42 GMT
• Title: Media Framing through the Lens of Event-Centric Narratives
• Authors: Rohan Das, Aditya Chandra, I-Ta Lee, Maria Leonor Pacheco,
• Abstract summary: We argue that to explain framing devices we have to look at the way narratives are constructed. We show that our framework can be used to analyze framing in U.S. news for two different domains: immigration and gun control.
• Score: 5.991851254194096
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: From a communications perspective, a frame defines the packaging of the language used in such a way as to encourage certain interpretations and to discourage others. For example, a news article can frame immigration as either a boost or a drain on the economy, and thus communicate very different interpretations of the same phenomenon. In this work, we argue that to explain framing devices we have to look at the way narratives are constructed. As a first step in this direction, we propose a framework that extracts events and their relations to other events, and groups them into high-level narratives that help explain frames in news articles. We show that our framework can be used to analyze framing in U.S. news for two different domains: immigration and gun control.

Related papers

• Narratives at Conflict: Computational Analysis of News Framing in Multilingual Disinformation Campaigns [22.05782818652258]
  We explore how multilingual framing of the same issue differs systematically. We use eight years of Russia-backed disinformation campaigns, spanning 8k news articles in 4 languages targeting 15 countries. We find that disinformation campaigns consistently and intentionally favor specific framing, depending on the target language of the audience.
  arXiv  Detail & Related papers  (2024-08-24T18:51:47Z)
• Detecting Frames in News Headlines and Lead Images in U.S. Gun Violence Coverage [12.484533062130453]
  We study the value of combining lead images and contextual information with text to identify the frame of a news article. We release the first multimodal news framing dataset related to gun violence in the U.S.
  arXiv  Detail & Related papers  (2024-06-25T01:56:47Z)
• Framing Analysis of Health-Related Narratives: Conspiracy versus Mainstream Media [3.3181276611945263]
  We investigate how the framing of health-related topics, such as COVID-19 and other diseases, differs between conspiracy and mainstream websites. We find that health-related narratives in conspiracy media are predominantly framed in terms of beliefs, while mainstream media tend to present them in terms of science.
  arXiv  Detail & Related papers  (2024-01-18T14:56:23Z)
• FAMuS: Frames Across Multiple Sources [74.03795560933612]
  FAMuS is a new corpus of Wikipedia passages that emphreport on some event, paired with underlying, genre-diverse (non-Wikipedia) emphsource articles for the same event. We present results on two key event understanding tasks enabled by FAMuS.
  arXiv  Detail & Related papers  (2023-11-09T18:57:39Z)
• Open-Domain Event Graph Induction for Mitigating Framing Bias [89.46744219887005]
  We argue that studying and identifying framing bias is a crucial step towards trustworthy event understanding. We propose a novel task, neutral event graph induction, to address this problem. Our task aims to induce such structural knowledge with minimal framing bias in an open domain.
  arXiv  Detail & Related papers  (2023-05-22T08:57:42Z)
• Discourse Analysis for Evaluating Coherence in Video Paragraph Captions [99.37090317971312]
  We are exploring a novel discourse based framework to evaluate the coherence of video paragraphs. Central to our approach is the discourse representation of videos, which helps in modeling coherence of paragraphs conditioned on coherence of videos. Our experiment results have shown that the proposed framework evaluates coherence of video paragraphs significantly better than all the baseline methods.
  arXiv  Detail & Related papers  (2022-01-17T04:23:08Z)
• Controlled Neural Sentence-Level Reframing of News Articles [40.802766338425926]
  We study how to computationally reframe sentences in news articles while maintaining their coherence to the context. We propose three strategies: framed-language preservation pretraining, named-entity, and adversarial learning. Our results indicate that generating properly-framed text works well but with tradeoffs.
  arXiv  Detail & Related papers  (2021-09-10T15:57:24Z)
• Paragraph-level Commonsense Transformers with Recurrent Memory [77.4133779538797]
  We train a discourse-aware model that incorporates paragraph-level information to generate coherent commonsense inferences from narratives. Our results show that PARA-COMET outperforms the sentence-level baselines, particularly in generating inferences that are both coherent and novel.
  arXiv  Detail & Related papers  (2020-10-04T05:24:12Z)
• Moral Framing and Ideological Bias of News [2.72905758998493]
  We capture the frames based on the Moral Foundation Theory. We propose an unsupervised method that extracts the framing Bias and the framing Intensity. We validate the performance on an annotated twitter dataset and then use it to quantify the framing bias and partisanship of news.
  arXiv  Detail & Related papers  (2020-09-27T23:36:14Z)
• VisualCOMET: Reasoning about the Dynamic Context of a Still Image [97.20800299330078]
  We propose VisualComet, a framework for visual commonsense reasoning. VisualComet predicts events that might have happened before, events that might happen next, and the intents of the people at present. We introduce the first large-scale repository of Visual Commonsense Graphs.
  arXiv  Detail & Related papers  (2020-04-22T19:02:20Z)
• A Deep Neural Framework for Contextual Affect Detection [51.378225388679425]
  A short and simple text carrying no emotion can represent some strong emotions when reading along with its context. We propose a Contextual Affect Detection framework which learns the inter-dependence of words in a sentence.
  arXiv  Detail & Related papers  (2020-01-28T05:03:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.