Mapping the Media Landscape: Predicting Factual Reporting and Political Bias Through Web Interactions

• URL: http://arxiv.org/abs/2410.17655v1
• Date: Wed, 23 Oct 2024 08:18:26 GMT
• Title: Mapping the Media Landscape: Predicting Factual Reporting and Political Bias Through Web Interactions
• Authors: Dairazalia Sánchez-Cortés, Sergio Burdisso, Esaú Villatoro-Tello, Petr Motlicek,
• Abstract summary: We propose an extension to a recently presented news media reliability estimation method. We assess the classification performance of four reinforcement learning strategies on a large news media hyperlink graph. Our experiments, targeting two challenging bias descriptors, factual reporting and political bias, showed a significant performance improvement at the source media level.
• Score: 0.7249731529275342
• License:
• Abstract: Bias assessment of news sources is paramount for professionals, organizations, and researchers who rely on truthful evidence for information gathering and reporting. While certain bias indicators are discernible from content analysis, descriptors like political bias and fake news pose greater challenges. In this paper, we propose an extension to a recently presented news media reliability estimation method that focuses on modeling outlets and their longitudinal web interactions. Concretely, we assess the classification performance of four reinforcement learning strategies on a large news media hyperlink graph. Our experiments, targeting two challenging bias descriptors, factual reporting and political bias, showed a significant performance improvement at the source media level. Additionally, we validate our methods on the CLEF 2023 CheckThat! Lab challenge, outperforming the reported results in both, F1-score and the official MAE metric. Furthermore, we contribute by releasing the largest annotated dataset of news source media, categorized with factual reporting and political bias labels. Our findings suggest that profiling news media sources based on their hyperlink interactions over time is feasible, offering a bird's-eye view of evolving media landscapes.

Related papers

• 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)
• Computational Assessment of Hyperpartisanship in News Titles [55.92100606666497]
  We first adopt a human-guided machine learning framework to develop a new dataset for hyperpartisan news title detection. Overall the Right media tends to use proportionally more hyperpartisan titles. We identify three major topics including foreign issues, political systems, and societal issues that are suggestive of hyperpartisanship in news titles.
  arXiv  Detail & Related papers  (2023-01-16T05:56:58Z)
• 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)
• Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks [49.29141811578359]
  We propose to leverage a hypergraph to represent group-wise interaction among news, while focusing on important news relations with its dual-level attention mechanism. Our approach yields remarkable performance and maintains the high performance even with a small subset of labeled news data.
  arXiv  Detail & Related papers  (2022-12-24T00:19:32Z)
• NeuS: Neutral Multi-News Summarization for Mitigating Framing Bias [54.89737992911079]
  We propose a new task, a neutral summary generation from multiple news headlines of the varying political spectrum. One of the most interesting observations is that generation models can hallucinate not only factually inaccurate or unverifiable content, but also politically biased content.
  arXiv  Detail & Related papers  (2022-04-11T07:06:01Z)
• Newsalyze: Effective Communication of Person-Targeting Biases in News Articles [8.586057042714698]
  We present a system for bias identification, which combines state-of-the-art methods from natural language understanding. Second, we devise bias-sensitive visualizations to communicate bias in news articles to non-expert news consumers. Third, our main contribution is a large-scale user study that measures bias-awareness in a setting that approximates daily news consumption.
  arXiv  Detail & Related papers  (2021-10-18T10:23:19Z)
• MBIC -- A Media Bias Annotation Dataset Including Annotator Characteristics [0.0]
  Media bias, or slanted news coverage, can have a substantial impact on public perception of events. In this poster, we present a matrix-based methodology to crowdsource such data using a self-developed annotation platform. We also present MBIC - the first sample of 1,700 statements representing various media bias instances.
  arXiv  Detail & Related papers  (2021-05-20T15:05:17Z)
• Newsalyze: Enabling News Consumers to Understand Media Bias [7.652448987187803]
  Knowing a news article's slant and authenticity is of crucial importance in times of "fake news" We introduce Newsalyze, a bias-aware news reader focusing on a subtle, yet powerful form of media bias, named bias by word choice and labeling (WCL) WCL bias can alter the assessment of entities reported in the news, e.g., "freedom fighters" vs. "terrorists"
  arXiv  Detail & Related papers  (2021-05-20T11:20:37Z)
• Enabling News Consumers to View and Understand Biased News Coverage: A Study on the Perception and Visualization of Media Bias [7.092487352312782]
  We create three manually annotated datasets and test varying visualization strategies. Results show no strong effects of becoming aware of the bias of the treatment groups compared to the control group. Using a multilevel model, we find that perceived journalist bias is significantly related to perceived political extremeness and impartiality of the article.
  arXiv  Detail & Related papers  (2021-05-20T10:16:54Z)

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.