Framing in the Presence of Supporting Data: A Case Study in U.S. Economic News

• URL: http://arxiv.org/abs/2402.14224v3
• Date: Thu, 17 Oct 2024 18:09:40 GMT
• Title: Framing in the Presence of Supporting Data: A Case Study in U.S. Economic News
• Authors: Alexandria Leto, Elliot Pickens, Coen D. Needell, David Rothschild, Maria Leonor Pacheco,
• Abstract summary: We argue that there are newsworthy topics where objective measures exist in the form of supporting data. We propose a computational framework to analyze editorial choices in this setup.
• Score: 43.51164834482535
• License:
• Abstract: The mainstream media has much leeway in what it chooses to cover and how it covers it. These choices have real-world consequences on what people know and their subsequent behaviors. However, the lack of objective measures to evaluate editorial choices makes research in this area particularly difficult. In this paper, we argue that there are newsworthy topics where objective measures exist in the form of supporting data and propose a computational framework to analyze editorial choices in this setup. We focus on the economy because the reporting of economic indicators presents us with a relatively easy way to determine both the selection and framing of various publications. Their values provide a ground truth of how the economy is doing relative to how the publications choose to cover it. To do this, we define frame prediction as a set of interdependent tasks. At the article level, we learn to identify the reported stance towards the general state of the economy. Then, for every numerical quantity reported in the article, we learn to identify whether it corresponds to an economic indicator and whether it is being reported in a positive or negative way. To perform our analysis, we track six American publishers and each article that appeared in the top 10 slots of their landing page between 2015 and 2023.

Related papers

• The Impact of Differential Feature Under-reporting on Algorithmic Fairness [86.275300739926]
  We present an analytically tractable model of differential feature under-reporting. We then use to characterize the impact of this kind of data bias on algorithmic fairness. Our results show that, in real world data settings, under-reporting typically leads to increasing disparities.
  arXiv  Detail & Related papers  (2024-01-16T19:16:22Z)
• Tracking the Newsworthiness of Public Documents [107.12303391111014]
  This work focuses on news coverage of local public policy in the San Francisco Bay Area by the San Francisco Chronicle. First, we gather news articles, public policy documents and meeting recordings and link them using probabilistic relational modeling. Second, we define a new task: newsworthiness prediction, to predict if a policy item will get covered.
  arXiv  Detail & Related papers  (2023-11-16T10:05:26Z)
• Towards Corpus-Scale Discovery of Selection Biases in News Coverage: Comparing What Sources Say About Entities as a Start [65.28355014154549]
  This paper investigates the challenges of building scalable NLP systems for discovering patterns of media selection biases directly from news content in massive-scale news corpora. We show the capabilities of the framework through a case study on NELA-2020, a corpus of 1.8M news articles in English from 519 news sources worldwide.
  arXiv  Detail & Related papers  (2023-04-06T23:36:45Z)
• Bias or Diversity? Unraveling Fine-Grained Thematic Discrepancy in U.S. News Headlines [63.52264764099532]
  We use a large dataset of 1.8 million news headlines from major U.S. media outlets spanning from 2014 to 2022. We quantify the fine-grained thematic discrepancy related to four prominent topics - domestic politics, economic issues, social issues, and foreign affairs. Our findings indicate that on domestic politics and social issues, the discrepancy can be attributed to a certain degree of media bias.
  arXiv  Detail & Related papers  (2023-03-28T03:31:37Z)
• Unveiling the Hidden Agenda: Biases in News Reporting and Consumption [59.55900146668931]
  We build a six-year dataset on the Italian vaccine debate and adopt a Bayesian latent space model to identify narrative and selection biases. We found a nonlinear relationship between biases and engagement, with higher engagement for extreme positions. Analysis of news consumption on Twitter reveals common audiences among news outlets with similar ideological positions.
  arXiv  Detail & Related papers  (2023-01-14T18:58:42Z)
• Forecasting with Economic News [0.9281671380673304]
  We consider only the text in the article that is semantically dependent on a term of interest. We find that several measures of economic sentiment track closely business cycle fluctuations. We also find that sentiment matters to explains the tails of the probability distribution across several macroeconomic variables.
  arXiv  Detail & Related papers  (2022-03-29T15:46:42Z)
• How to Effectively Identify and Communicate Person-Targeting Media Bias in Daily News Consumption? [8.586057042714698]
  We present an in-progress system for news recommendation that is the first to automate the manual procedure of content analysis. Our recommender detects and reveals substantial frames that are actually present in individual news articles. Our study shows that recommending news articles that differently frame an event significantly improves respondents' awareness of bias.
  arXiv  Detail & Related papers  (2021-10-18T10:13:23Z)
• Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity [35.19976910093135]
  The research presented in this paper addresses not only the automatic detection of bias but goes one step further in that it explores how political bias and unfairness are manifested linguistically. We utilize a new corpus of 6964 news articles with labels derived from adfontesmedia.com and develop a neural model for bias assessment.
  arXiv  Detail & Related papers  (2020-10-20T22:25:00Z)

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.